CDC Daily by State
davegoblue
8/3/2021
Background
This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.
Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.
The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.
The code in this module builds on code available in _v002 to include vaccines data:
- Generic_Added_Utility_Functions_202105_v001.R - generic functions that can be used in other areas
- Coronavirus_CDC_Daily_Functions_v001.R - functions specific to coronavirus daily data
Broadly, the CDC data analyzed by this module includes:
- CDC case and death data by state and date are available for download on the CDC website
- CDC hospital data are available for download on the healthdata.gov website
- CDC vaccines data are also available for download on the CDC website:
Functions and Mapping Files
The tidyverse package is loaded and functions are sourced:
# The tidyverse functions are routinely used without package::function format
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v001.R")A series of mapping files are also available to allow for parameterized processing. Mappings include:
- urlMapper - mapping file for urlType and url location to download data
- renMapper - mapping file for renaming of variables in the raw data file
- selfListMapper - mapping file for transformations by variable type
- fullListMapper - mapping file for transformations across variable types
- lstComboMapper - mapping file for elements to be combined by data type (most common is to combine NYC and NYS data to NY if the file provides them separately)
- uqMapper - mapping file for fields that should combine to be unique keys for processed files
- lstFilterMapper - mapping file for filtering to subset (most common is to keep 50 states and DC)
- vecSelectMapper - mapping file for variables to keep
- checkControlGroupMapper - mapping file for group_by() of control total checks
- checkControlAggMapper - mapping file for numeric variables for control total checks
- checkSimilarityMapper - mapping file for similarity checks to perform
- plotSimilarityMaooer - mapping file for fields where differences in universe should be plotted
- keyAggMapper - mapping file for the aggregate-level control total checks to perform
- perCapMapper - named vector that drives conversion from original field name to per capita field name
These default parameters are maintained in a separate .R file and can be sourced:
source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")Example for Comparison to Previous
The function is tested on existing, previously downloaded data:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210801.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210801.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210801.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210708")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210708")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210728_vaxonly")$dfRaw$vax
)
cdc_daily_210801_test <- readRunCDCDaily(thruLabel="Jul 31, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 143 152 9 0.06101695
## 2 2020-02-03 tot_deaths 143 152 9 0.06101695
## 3 2020-02-04 tot_deaths 143 152 9 0.06101695
## 4 2020-02-05 tot_deaths 143 152 9 0.06101695
## 5 2020-02-06 tot_deaths 143 152 9 0.06101695
## 6 2020-02-07 tot_deaths 143 152 9 0.06101695
## 7 2020-02-08 tot_deaths 144 153 9 0.06060606
## 8 2020-02-09 tot_deaths 144 153 9 0.06060606
## 9 2020-02-10 tot_deaths 144 153 9 0.06060606
## 10 2020-02-11 tot_deaths 144 153 9 0.06060606
## 11 2020-02-12 tot_deaths 144 153 9 0.06060606
## 12 2020-02-13 tot_deaths 144 153 9 0.06060606
## 13 2020-02-14 tot_deaths 144 153 9 0.06060606
## 14 2020-02-15 tot_deaths 144 153 9 0.06060606
## 15 2020-02-16 tot_deaths 144 153 9 0.06060606
## 16 2020-02-17 tot_deaths 144 153 9 0.06060606
## 17 2020-02-18 tot_deaths 144 153 9 0.06060606
## 18 2020-02-19 tot_deaths 145 154 9 0.06020067
## 19 2020-02-20 tot_deaths 145 154 9 0.06020067
## 20 2020-02-21 tot_deaths 145 154 9 0.06020067
## 21 2020-02-22 tot_deaths 145 154 9 0.06020067
## 22 2020-02-23 tot_deaths 145 154 9 0.06020067
## 23 2020-02-24 tot_deaths 145 154 9 0.06020067
## 24 2020-02-25 tot_deaths 145 154 9 0.06020067
## 25 2020-02-26 tot_deaths 145 154 9 0.06020067
## 26 2020-02-27 tot_deaths 146 155 9 0.05980066
## 27 2020-02-28 tot_deaths 146 155 9 0.05980066
## 28 2020-02-29 tot_deaths 147 156 9 0.05940594
## 29 2020-03-01 tot_deaths 147 156 9 0.05940594
## 30 2020-03-02 tot_deaths 153 162 9 0.05714286
## 31 2020-03-03 tot_deaths 156 165 9 0.05607477
## 32 2020-03-04 tot_deaths 158 167 9 0.05538462
## 33 2020-03-05 tot_deaths 160 169 9 0.05471125
## 34 2020-03-06 tot_deaths 163 172 9 0.05373134
## 35 2020-03-07 tot_deaths 168 177 9 0.05217391
## 36 2020-03-08 tot_deaths 173 182 9 0.05070423
## 37 2020-02-02 tot_cases 510 612 102 0.18181818
## 38 2020-02-03 tot_cases 542 644 102 0.17200675
## 39 2020-02-04 tot_cases 550 652 102 0.16971714
## 40 2020-02-05 tot_cases 555 657 102 0.16831683
## 41 2020-02-06 tot_cases 557 658 101 0.16625514
## 42 2020-02-07 tot_cases 562 663 101 0.16489796
## 43 2020-02-08 tot_cases 570 670 100 0.16129032
## 44 2020-02-09 tot_cases 605 705 100 0.15267176
## 45 2020-02-10 tot_cases 614 713 99 0.14920874
## 46 2020-02-11 tot_cases 625 721 96 0.14264487
## 47 2020-02-12 tot_cases 635 731 96 0.14055637
## 48 2020-02-13 tot_cases 641 736 95 0.13798112
## 49 2020-02-14 tot_cases 649 743 94 0.13505747
## 50 2020-02-15 tot_cases 654 748 94 0.13409415
## 51 2020-02-16 tot_cases 667 758 91 0.12771930
## 52 2020-02-17 tot_cases 685 776 91 0.12457221
## 53 2020-02-18 tot_cases 692 783 91 0.12338983
## 54 2020-02-19 tot_cases 709 799 90 0.11936340
## 55 2020-02-20 tot_cases 723 811 88 0.11473272
## 56 2020-02-21 tot_cases 742 829 87 0.11075748
## 57 2020-02-22 tot_cases 768 855 87 0.10720887
## 58 2020-02-23 tot_cases 792 877 85 0.10185740
## 59 2020-02-24 tot_cases 811 896 85 0.09958992
## 60 2020-02-25 tot_cases 835 920 85 0.09686610
## 61 2020-02-26 tot_cases 879 963 84 0.09120521
## 62 2020-02-27 tot_cases 916 998 82 0.08568443
## 63 2020-02-28 tot_cases 968 1049 81 0.08031730
## 64 2020-02-29 tot_cases 1005 1087 82 0.07839388
## 65 2020-03-01 tot_cases 1094 1177 83 0.07309555
## 66 2020-03-02 tot_cases 1172 1254 82 0.06760099
## 67 2020-03-03 tot_cases 1343 1424 81 0.05854716
## 68 2020-03-04 tot_cases 1482 1565 83 0.05447982
## 69 2021-07-05 new_deaths 104 37 67 0.95035461
## 70 2021-07-04 new_deaths 98 38 60 0.88235294
## 71 2021-01-18 new_deaths 2674 1130 1544 0.81177708
## 72 2021-07-03 new_deaths 140 86 54 0.47787611
## 73 2021-01-19 new_deaths 3036 4578 1542 0.40504334
## 74 2020-12-26 new_deaths 2248 3093 845 0.31642015
## 75 2020-12-24 new_deaths 3274 2463 811 0.28272616
## 76 2021-06-27 new_deaths 139 105 34 0.27868852
## 77 2021-06-20 new_deaths 176 145 31 0.19314642
## 78 2021-06-26 new_deaths 172 142 30 0.19108280
## 79 2021-06-19 new_deaths 180 154 26 0.15568862
## 80 2021-06-28 new_deaths 193 170 23 0.12672176
## 81 2021-06-24 new_deaths 287 258 29 0.10642202
## 82 2021-06-17 new_deaths 334 302 32 0.10062893
## 83 2021-06-23 new_deaths 310 281 29 0.09813875
## 84 2021-06-25 new_deaths 300 273 27 0.09424084
## 85 2021-06-22 new_deaths 283 258 25 0.09242144
## 86 2021-06-18 new_deaths 210 192 18 0.08955224
## 87 2021-06-13 new_deaths 200 184 16 0.08333333
## 88 2021-05-30 new_deaths 237 220 17 0.07439825
## 89 2020-03-21 new_deaths 114 107 7 0.06334842
## 90 2021-06-11 new_deaths 326 306 20 0.06329114
## 91 2021-06-16 new_deaths 310 293 17 0.05638474
## 92 2021-06-15 new_deaths 336 319 17 0.05190840
## 93 2020-02-02 new_cases 1 557 556 1.99283154
## 94 2021-07-05 new_cases 11563 3575 7988 1.05535738
## 95 2021-07-04 new_cases 12794 4156 8638 1.01923304
## 96 2021-07-03 new_cases 14978 5887 9091 0.87141145
## 97 2021-06-10 new_cases 16732 12363 4369 0.30032652
## 98 2021-01-18 new_cases 138853 107646 31207 0.25320184
## 99 2021-01-19 new_cases 145009 176292 31283 0.19472706
## 100 2021-07-02 new_cases 16830 14183 2647 0.17070261
## 101 2021-06-20 new_cases 9228 7787 1441 0.16937996
## 102 2020-12-24 new_cases 222824 195402 27422 0.13113484
## 103 2021-06-01 new_cases 9689 8540 1149 0.12606287
## 104 2021-01-29 new_cases 156344 139722 16622 0.11228577
## 105 2020-12-26 new_cases 151874 169350 17476 0.10880881
## 106 2021-06-30 new_cases 17295 15526 1769 0.10779684
## 107 2021-06-28 new_cases 9690 8701 989 0.10755261
## 108 2021-06-09 new_cases 19404 21526 2122 0.10368923
## 109 2021-01-09 new_cases 249812 226455 23357 0.09808364
## 110 2021-01-30 new_cases 137321 150808 13487 0.09361779
## 111 2021-07-01 new_cases 18730 17149 1581 0.08812955
## 112 2021-06-08 new_cases 14356 15667 1311 0.08733304
## 113 2021-06-29 new_cases 16159 15051 1108 0.07100288
## 114 2021-06-06 new_cases 12102 11304 798 0.06818764
## 115 2020-07-14 new_cases 65684 61818 3866 0.06064219
## 116 2021-01-08 new_cases 295289 312357 17068 0.05617745
## 117 2021-05-24 new_cases 15657 14828 829 0.05438740
## 118 2021-05-31 new_cases 9193 9700 507 0.05367067
## 119 2021-05-03 new_cases 33239 31601 1638 0.05052437
## 120 2021-06-07 new_cases 10122 10644 522 0.05027449
## 121 2020-07-15 new_cases 70320 73939 3619 0.05017365
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IN tot_deaths 3407157 3378244 28913 0.008522120
## 2 SC tot_deaths 2291589 2305862 14273 0.006209093
## 3 CA tot_deaths 14183041 14129523 53518 0.003780512
## 4 NC tot_deaths 3073917 3062861 11056 0.003603194
## 5 MS tot_deaths 1998075 1991323 6752 0.003384972
## 6 KY tot_deaths 1634463 1630052 4411 0.002702392
## 7 RI tot_deaths 749883 751479 1596 0.002126070
## 8 NM tot_deaths 1001515 999916 1599 0.001597857
## 9 AL tot_deaths 2742024 2738028 3996 0.001458380
## 10 CA tot_cases 865747767 837321729 28426038 0.033382123
## 11 SC tot_cases 129358076 129977727 619651 0.004778754
## 12 RI tot_cases 32453898 32591078 137180 0.004218004
## 13 AL tot_cases 131847795 131406619 441176 0.003351708
## 14 MI tot_cases 214132223 214386719 254496 0.001187793
## 15 MS tot_cases 77187328 77104046 83282 0.001079542
## 16 MS new_deaths 7432 7332 100 0.013546464
## 17 NM new_deaths 4382 4344 38 0.008709603
## 18 CA new_deaths 63517 62992 525 0.008299805
## 19 KY new_deaths 7285 7229 56 0.007716687
## 20 NC new_deaths 13517 13434 83 0.006159326
## 21 AL new_deaths 11430 11360 70 0.006143045
## 22 MI new_deaths 21076 20995 81 0.003850633
## 23 IN new_deaths 13914 13863 51 0.003672103
## 24 TX new_deaths 51507 51349 158 0.003072256
## 25 TN new_deaths 12611 12576 35 0.002779211
## 26 WA new_deaths 5954 5939 15 0.002522492
## 27 RI new_deaths 2736 2730 6 0.002195390
## 28 UT new_deaths 2371 2368 3 0.001266090
## 29 CA new_cases 3880232 3713944 166288 0.043793560
## 30 VI new_cases 3932 3916 16 0.004077472
## 31 MS new_cases 323003 321780 1223 0.003793524
## 32 AL new_cases 554270 552325 1945 0.003515288
## 33 LA new_cases 483605 482096 1509 0.003125191
## 34 NV new_cases 335771 334763 1008 0.003006559
## 35 FL new_cases 2344516 2337613 6903 0.002948659
## 36 WY new_cases 62592 62445 147 0.002351304
## 37 UT new_cases 416971 416110 861 0.002067026
## 38 KS new_cases 319154 318515 639 0.002004175
## 39 WA new_cases 453368 452483 885 0.001953964
## 40 AK new_cases 68595 68478 117 0.001707120
## 41 MI new_cases 1002081 1000375 1706 0.001703908
## 42 OR new_cases 209377 209035 342 0.001634752
## 43 NC new_cases 1015407 1014359 1048 0.001032631
##
##
##
## Raw file for cdcDaily:
## Rows: 33,360
## Columns: 15
## $ date <date> 2021-02-02, 2020-07-30, 2020-05-03, 2020-12-04, 2021-0~
## $ state <chr> "IL", "ME", "NH", "IN", "CA", "GU", "CT", "WI", "NV", "~
## $ tot_cases <dbl> 1130917, 3910, 2518, 367338, 3409079, 0, 267337, 98440,~
## $ conf_cases <dbl> 1130917, 3497, NA, NA, 3285871, NA, 250915, 92712, NA, ~
## $ prob_cases <dbl> 0, 413, NA, NA, 123208, NA, 16422, 5728, NA, 105447, NA~
## $ new_cases <dbl> 2304, 22, 89, 7899, 18703, 0, 0, 1502, 128, 199, 0, 394~
## $ pnew_case <dbl> 0, 2, 0, 0, 892, NA, 0, 94, 0, 47, NA, 5, 102, NA, 0, 0~
## $ tot_deaths <dbl> 21336, 123, 86, 7031, 49603, 0, 7381, 1237, 5586, 21047~
## $ conf_death <dbl> 19306, 122, NA, 6746, 49603, NA, 6049, 1228, NA, 19789,~
## $ prob_death <dbl> 2030, 1, NA, 285, 0, NA, 1332, 9, NA, 1258, NA, NA, 0, ~
## $ new_deaths <dbl> 63, 2, 2, 91, 494, 0, 0, 8, 0, 6, 0, 32, 60, 6, 2, 39, ~
## $ pnew_death <dbl> 16, 0, 0, 1, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 7, 0, ~
## $ created_at <chr> "02/03/2021 02:55:58 PM", "07/31/2020 02:35:06 PM", "05~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", "Not agree", "Agree", "N~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", "Agree", "Agree", "Not a~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 hosp_ped 662 597 65 0.10325655
## 2 2021-07-03 hosp_ped 638 597 41 0.06639676
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 523814 518483 5331 0.010229330
## 2 TN inp 558512 559654 1142 0.002042631
## 3 NM inp 137802 137991 189 0.001370593
## 4 NH hosp_ped 271 361 90 0.284810127
## 5 ME hosp_ped 452 509 57 0.118626431
## 6 KY hosp_ped 5518 5308 210 0.038795492
## 7 MA hosp_ped 5015 5201 186 0.036413469
## 8 AR hosp_ped 5977 5840 137 0.023186934
## 9 TN hosp_ped 7924 8102 178 0.022213902
## 10 DE hosp_ped 1647 1683 36 0.021621622
## 11 AL hosp_ped 7711 7555 156 0.020437574
## 12 WV hosp_ped 2226 2269 43 0.019132369
## 13 KS hosp_ped 1711 1679 32 0.018879056
## 14 NV hosp_ped 1999 2037 38 0.018830525
## 15 AZ hosp_ped 11435 11266 169 0.014889212
## 16 VA hosp_ped 6604 6513 91 0.013875124
## 17 IN hosp_ped 6913 6826 87 0.012664677
## 18 MS hosp_ped 3727 3686 41 0.011061648
## 19 MO hosp_ped 15406 15241 165 0.010767775
## 20 SC hosp_ped 2706 2679 27 0.010027855
## 21 PA hosp_ped 19857 20010 153 0.007675521
## 22 WA hosp_ped 4288 4263 25 0.005847269
## 23 NM hosp_ped 3125 3107 18 0.005776637
## 24 IA hosp_ped 2275 2287 12 0.005260851
## 25 CO hosp_ped 9355 9401 46 0.004905097
## 26 NJ hosp_ped 9108 9142 34 0.003726027
## 27 OH hosp_ped 25500 25406 94 0.003693081
## 28 IL hosp_ped 19711 19644 67 0.003404904
## 29 GA hosp_ped 21902 21973 71 0.003236467
## 30 MT hosp_ped 1022 1025 3 0.002931119
## 31 PR hosp_ped 11353 11380 27 0.002375401
## 32 CA hosp_ped 30719 30667 52 0.001694197
## 33 LA hosp_ped 3174 3179 5 0.001574059
## 34 TX hosp_ped 38680 38739 59 0.001524174
## 35 FL hosp_ped 54840 54921 81 0.001475934
## 36 HI hosp_ped 720 721 1 0.001387925
## 37 NC hosp_ped 10619 10606 13 0.001224971
## 38 AL hosp_adult 443621 439848 3773 0.008541330
## 39 TN hosp_adult 494022 494969 947 0.001915083
## 40 NM hosp_adult 112634 112842 208 0.001844986
## 41 ME hosp_adult 37173 37121 52 0.001399844
## 42 WV hosp_adult 126618 126444 174 0.001375157
## 43 KY hosp_adult 299353 299757 404 0.001348667
## 44 NH hosp_adult 39064 39014 50 0.001280771
## 45 CA hosp_adult 2422197 2425080 2883 0.001189534
##
##
##
## Raw file for cdcHosp:
## Rows: 27,682
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_210801.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 14,918
## Columns: 69
## $ date <date> 2021-07-31, 2021-07-31, 2021-0~
## $ MMWR_week <dbl> 30, 30, 30, 30, 30, 30, 30, 30,~
## $ state <chr> "AK", "NM", "PR", "RP", "MS", "~
## $ Distributed <dbl> 854805, 2449685, 4266370, 28650~
## $ Distributed_Janssen <dbl> 59300, 138500, 190000, 3800, 16~
## $ Distributed_Moderna <dbl> 366220, 1066860, 1853400, 20800~
## $ Distributed_Pfizer <dbl> 429285, 1244325, 2222970, 4050,~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 116849, 116828, 133587, 159993,~
## $ Distributed_Per_100k_12Plus <dbl> 140390, 137051, 149787, 187353,~
## $ Distributed_Per_100k_18Plus <dbl> 154979, 151123, 162779, 205421,~
## $ Distributed_Per_100k_65Plus <dbl> 933315, 648741, 785808, 944298,~
## $ vxa <dbl> 697440, 2487536, 3975244, 26286~
## $ Administered_12Plus <dbl> 695366, 2487278, 3973905, 26286~
## $ Administered_18Plus <dbl> 653874, 2330829, 3705211, 25597~
## $ Administered_65Plus <dbl> 143058, 664378, 1048492, 3120, ~
## $ Administered_Janssen <dbl> 28437, 87616, 114212, 2145, 637~
## $ Administered_Moderna <dbl> 289116, 1081538, 1691040, 23441~
## $ Administered_Pfizer <dbl> 379706, 1313894, 2169642, 700, ~
## $ Administered_Unk_Manuf <dbl> 181, 4488, 350, 0, 1047, 727, 0~
## $ Administered_Fed_LTC <dbl> 6640, 39710, 74284, 0, 54224, 1~
## $ Administered_Fed_LTC_Residents <dbl> 2078, 11847, 11431, 0, 26288, 8~
## $ Administered_Fed_LTC_Staff <dbl> 1378, 12139, 10950, 0, 12915, 5~
## $ Administered_Fed_LTC_Unk <dbl> 3184, 15724, 51903, 0, 15021, 3~
## $ Administered_Fed_LTC_Dose1 <dbl> 4300, 24065, 53094, 0, 31843, 1~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 1383, 6414, 7925, 0, 14433, 507~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 956, 6649, 7461, 0, 7685, 36183~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 1961, 11002, 37708, 0, 9725, 29~
## $ Admin_Per_100k <dbl> 95338, 118633, 124472, 146792, ~
## $ Admin_Per_100k_12Plus <dbl> 114205, 139154, 139519, 171894,~
## $ Admin_Per_100k_18Plus <dbl> 118550, 143790, 141368, 183531,~
## $ Admin_Per_100k_65Plus <dbl> 156197, 175945, 193118, 102835,~
## $ Recip_Administered <dbl> 692140, 2511859, 4003254, 26519~
## $ Administered_Dose1_Recip <dbl> 376882, 1374231, 2197391, 15199~
## $ Administered_Dose1_Pop_Pct <dbl> 51.5, 65.5, 68.8, 84.9, 39.8, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 375603, 1373997, 2196318, 15199~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 61.7, 76.9, 77.1, 99.4, 46.9, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 352303, 1282733, 2045507, 14508~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 63.9, 79.1, 78.0, 99.9, 50.0, 6~
## $ Administered_Dose1_Recip_65Plus <dbl> 75867, 359328, 564728, 1707, 38~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 82.8, 95.2, 99.9, 56.3, 78.6, 9~
## $ vxc <dbl> 333092, 1198386, 1911719, 13461~
## $ vxcpoppct <dbl> 45.5, 57.2, 59.9, 75.2, 34.5, 5~
## $ Series_Complete_12Plus <dbl> 332299, 1198314, 1911408, 13461~
## $ Series_Complete_12PlusPop_Pct <dbl> 54.6, 67.0, 67.1, 88.0, 40.7, 6~
## $ vxcgte18 <dbl> 314089, 1126808, 1790751, 13461~
## $ vxcgte18pct <dbl> 56.9, 69.5, 68.3, 96.5, 44.0, 6~
## $ vxcgte65 <dbl> 71390, 324959, 506358, 1671, 35~
## $ vxcgte65pct <dbl> 77.9, 86.1, 93.3, 55.1, 72.7, 8~
## $ Series_Complete_Janssen <dbl> 26386, 86064, 113821, 2148, 627~
## $ Series_Complete_Moderna <dbl> 133838, 492367, 784508, 11283, ~
## $ Series_Complete_Pfizer <dbl> 172824, 618609, 1013352, 30, 52~
## $ Series_Complete_Unk_Manuf <dbl> 44, 1346, 38, 0, 138, 318, 0, 1~
## $ Series_Complete_Janssen_12Plus <dbl> 26384, 86052, 113778, 2148, 627~
## $ Series_Complete_Moderna_12Plus <dbl> 133834, 492349, 784456, 11283, ~
## $ Series_Complete_Pfizer_12Plus <dbl> 172037, 618567, 1013136, 30, 52~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 44, 1346, 38, 0, 138, 318, 0, 1~
## $ Series_Complete_Janssen_18Plus <dbl> 26261, 85937, 113673, 2148, 626~
## $ Series_Complete_Moderna_18Plus <dbl> 133469, 492021, 784182, 11283, ~
## $ Series_Complete_Pfizer_18Plus <dbl> 154318, 547516, 892859, 30, 495~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 41, 1334, 37, 0, 133, 309, 0, 1~
## $ Series_Complete_Janssen_65Plus <dbl> 2638, 18271, 19965, 212, 15374,~
## $ Series_Complete_Moderna_65Plus <dbl> 40125, 151102, 257623, 1450, 18~
## $ Series_Complete_Pfizer_65Plus <dbl> 28605, 154857, 228764, 9, 15393~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 22, 729, 6, 0, 60, 135, 0, 734,~
## $ Series_Complete_FedLTC <dbl> 2320, 15515, 21185, 0, 22390, 6~
## $ Series_Complete_FedLTC_Residents <dbl> 676, 5246, 3503, 0, 11688, 3467~
## $ Series_Complete_FedLTC_Staff <dbl> 425, 5319, 3488, 0, 5176, 23209~
## $ Series_Complete_FedLTC_Unknown <dbl> 1219, 4950, 14194, 0, 5526, 782~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.21e+9 1.64e+8 3.49e+7 604596 32804
## 2 after 8.17e+9 1.63e+8 3.47e+7 601834 28356
## 3 pctchg 4.40e-3 3.96e-3 4.58e-3 0.00457 0.136
##
##
## Processed for cdcDaily:
## Rows: 28,356
## Columns: 6
## $ date <date> 2021-02-02, 2020-07-30, 2020-05-03, 2020-12-04, 2021-01-28~
## $ state <chr> "IL", "ME", "NH", "IN", "CA", "CT", "WI", "NV", "MI", "MI",~
## $ tot_cases <dbl> 1130917, 3910, 2518, 367338, 3409079, 267337, 98440, 324132~
## $ tot_deaths <dbl> 21336, 123, 86, 7031, 49603, 7381, 1237, 5586, 21047, 0, 11~
## $ new_cases <dbl> 2304, 22, 89, 7899, 18703, 0, 1502, 128, 199, 0, 394, 3436,~
## $ new_deaths <dbl> 63, 2, 2, 91, 494, 0, 8, 0, 6, 0, 32, 60, 6, 2, 39, 66, 0, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.78e+7 2.19e+7 471723 27682
## 2 after 2.77e+7 2.18e+7 459822 26679
## 3 pctchg 5.58e-3 5.57e-3 0.0252 0.0362
##
##
## Processed for cdcHosp:
## Rows: 26,679
## Columns: 5
## $ date <date> 2020-07-22, 2020-07-20, 2020-07-19, 2020-07-18, 2020-07-18~
## $ state <chr> "IA", "IA", "ND", "IA", "ND", "TX", "OK", "CT", "ND", "NM",~
## $ inp <dbl> 0, 1, 46, 10, 33, 12003, 678, 215, 16, 119, 51, 19, 250, 14~
## $ hosp_adult <dbl> 0, 1, NA, 10, NA, 7999, 566, 115, NA, NA, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> 0, 0, NA, 0, NA, 194, 9, 0, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.19e+10 3.37e+10 302401. 1.10e+10 561791. 3.28e+10 373517.
## 2 after 3.89e+10 1.63e+10 255914. 5.34e+ 9 512134. 1.59e+10 320269.
## 3 pctchg 5.24e- 1 5.16e- 1 0.154 5.16e- 1 0.0884 5.16e- 1 0.143
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 11,730
## Columns: 9
## $ date <date> 2021-07-31, 2021-07-31, 2021-07-31, 2021-07-31, 2021-07-3~
## $ state <chr> "AK", "NM", "MS", "WI", "NY", "OK", "MD", "NH", "WV", "AL"~
## $ vxa <dbl> 697440, 2487536, 2150026, 6163565, 22950250, 3460262, 7213~
## $ vxc <dbl> 333092, 1198386, 1026837, 3015017, 11109858, 1593194, 3559~
## $ vxcpoppct <dbl> 45.5, 57.2, 34.5, 51.8, 57.1, 40.3, 58.9, 58.3, 39.0, 34.3~
## $ vxcgte65 <dbl> 71390, 324959, 353642, 895738, 2663975, 482309, 844122, 22~
## $ vxcgte65pct <dbl> 77.9, 86.1, 72.7, 88.1, 80.8, 75.9, 88.0, 87.4, 70.2, 69.5~
## $ vxcgte18 <dbl> 314089, 1126808, 1001545, 2875753, 10577357, 1533309, 3342~
## $ vxcgte18pct <dbl> 56.9, 69.5, 44.0, 63.1, 68.6, 51.0, 71.0, 68.2, 46.9, 43.1~
##
## Integrated per capita data file:
## Rows: 28,569
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
all.equal(names(cdc_daily_210801_test), names(readFromRDS("cdc_daily_210801")))## [1] TRUEsapply(names(cdc_daily_210801_test), FUN=function(x)
identical(cdc_daily_210801_test[[x]], readFromRDS("cdc_daily_210801")[[x]])
)## stateData dfRaw dfProcess dfPerCapita useClusters plotDataList
## TRUE TRUE TRUE TRUE TRUE FALSEsapply(names(cdc_daily_210801_test$plotDataList), FUN=function(x)
identical(cdc_daily_210801_test$plotDataList[[x]], readFromRDS("cdc_daily_210801")$plotDataList[[x]])
)## dfFull dfAgg plotClusters summaryPlots detPlots
## TRUE TRUE TRUE FALSE FALSEAs expected, all data elements are identical. Plot environments change with each creation, so the plot objects are not identical.
The latest data are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210804.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210804.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210804.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210708")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210708")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210728_vaxonly")$dfRaw$vax
)
cdc_daily_210804 <- readRunCDCDaily(thruLabel="Aug 3, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 143 152 9 0.06101695
## 2 2020-02-03 tot_deaths 143 152 9 0.06101695
## 3 2020-02-04 tot_deaths 143 152 9 0.06101695
## 4 2020-02-05 tot_deaths 143 152 9 0.06101695
## 5 2020-02-06 tot_deaths 143 152 9 0.06101695
## 6 2020-02-07 tot_deaths 143 152 9 0.06101695
## 7 2020-02-08 tot_deaths 144 153 9 0.06060606
## 8 2020-02-09 tot_deaths 144 153 9 0.06060606
## 9 2020-02-10 tot_deaths 144 153 9 0.06060606
## 10 2020-02-11 tot_deaths 144 153 9 0.06060606
## 11 2020-02-12 tot_deaths 144 153 9 0.06060606
## 12 2020-02-13 tot_deaths 144 153 9 0.06060606
## 13 2020-02-14 tot_deaths 144 153 9 0.06060606
## 14 2020-02-15 tot_deaths 144 153 9 0.06060606
## 15 2020-02-16 tot_deaths 144 153 9 0.06060606
## 16 2020-02-17 tot_deaths 144 153 9 0.06060606
## 17 2020-02-18 tot_deaths 144 153 9 0.06060606
## 18 2020-02-19 tot_deaths 145 154 9 0.06020067
## 19 2020-02-20 tot_deaths 145 154 9 0.06020067
## 20 2020-02-21 tot_deaths 145 154 9 0.06020067
## 21 2020-02-22 tot_deaths 145 154 9 0.06020067
## 22 2020-02-23 tot_deaths 145 154 9 0.06020067
## 23 2020-02-24 tot_deaths 145 154 9 0.06020067
## 24 2020-02-25 tot_deaths 145 154 9 0.06020067
## 25 2020-02-26 tot_deaths 145 154 9 0.06020067
## 26 2020-02-27 tot_deaths 146 155 9 0.05980066
## 27 2020-02-28 tot_deaths 146 155 9 0.05980066
## 28 2020-02-29 tot_deaths 147 156 9 0.05940594
## 29 2020-03-01 tot_deaths 147 156 9 0.05940594
## 30 2020-03-02 tot_deaths 153 162 9 0.05714286
## 31 2020-03-03 tot_deaths 156 165 9 0.05607477
## 32 2020-03-04 tot_deaths 158 167 9 0.05538462
## 33 2020-03-05 tot_deaths 160 169 9 0.05471125
## 34 2020-03-06 tot_deaths 163 172 9 0.05373134
## 35 2020-03-07 tot_deaths 168 177 9 0.05217391
## 36 2020-03-08 tot_deaths 173 182 9 0.05070423
## 37 2020-02-02 tot_cases 510 612 102 0.18181818
## 38 2020-02-03 tot_cases 542 644 102 0.17200675
## 39 2020-02-04 tot_cases 550 652 102 0.16971714
## 40 2020-02-05 tot_cases 555 657 102 0.16831683
## 41 2020-02-06 tot_cases 557 658 101 0.16625514
## 42 2020-02-07 tot_cases 562 663 101 0.16489796
## 43 2020-02-08 tot_cases 570 670 100 0.16129032
## 44 2020-02-09 tot_cases 605 705 100 0.15267176
## 45 2020-02-10 tot_cases 614 713 99 0.14920874
## 46 2020-02-11 tot_cases 625 721 96 0.14264487
## 47 2020-02-12 tot_cases 635 731 96 0.14055637
## 48 2020-02-13 tot_cases 641 736 95 0.13798112
## 49 2020-02-14 tot_cases 649 743 94 0.13505747
## 50 2020-02-15 tot_cases 654 748 94 0.13409415
## 51 2020-02-16 tot_cases 667 758 91 0.12771930
## 52 2020-02-17 tot_cases 685 776 91 0.12457221
## 53 2020-02-18 tot_cases 692 783 91 0.12338983
## 54 2020-02-19 tot_cases 709 799 90 0.11936340
## 55 2020-02-20 tot_cases 723 811 88 0.11473272
## 56 2020-02-21 tot_cases 742 829 87 0.11075748
## 57 2020-02-22 tot_cases 768 855 87 0.10720887
## 58 2020-02-23 tot_cases 792 877 85 0.10185740
## 59 2020-02-24 tot_cases 811 896 85 0.09958992
## 60 2020-02-25 tot_cases 835 920 85 0.09686610
## 61 2020-02-26 tot_cases 879 963 84 0.09120521
## 62 2020-02-27 tot_cases 916 998 82 0.08568443
## 63 2020-02-28 tot_cases 968 1049 81 0.08031730
## 64 2020-02-29 tot_cases 1005 1087 82 0.07839388
## 65 2020-03-01 tot_cases 1094 1177 83 0.07309555
## 66 2020-03-02 tot_cases 1172 1254 82 0.06760099
## 67 2020-03-03 tot_cases 1343 1424 81 0.05854716
## 68 2020-03-04 tot_cases 1482 1565 83 0.05447982
## 69 2021-07-05 new_deaths 106 37 69 0.96503497
## 70 2021-07-04 new_deaths 101 38 63 0.90647482
## 71 2021-01-18 new_deaths 2674 1130 1544 0.81177708
## 72 2021-07-03 new_deaths 142 86 56 0.49122807
## 73 2021-01-19 new_deaths 3036 4578 1542 0.40504334
## 74 2020-12-26 new_deaths 2248 3093 845 0.31642015
## 75 2020-12-24 new_deaths 3274 2463 811 0.28272616
## 76 2021-06-27 new_deaths 139 105 34 0.27868852
## 77 2021-06-26 new_deaths 175 142 33 0.20820189
## 78 2021-06-20 new_deaths 177 145 32 0.19875776
## 79 2021-06-19 new_deaths 180 154 26 0.15568862
## 80 2021-06-28 new_deaths 193 170 23 0.12672176
## 81 2021-06-24 new_deaths 287 258 29 0.10642202
## 82 2021-06-17 new_deaths 334 302 32 0.10062893
## 83 2021-06-23 new_deaths 310 281 29 0.09813875
## 84 2021-06-25 new_deaths 300 273 27 0.09424084
## 85 2021-06-22 new_deaths 283 258 25 0.09242144
## 86 2021-06-18 new_deaths 210 192 18 0.08955224
## 87 2021-06-13 new_deaths 200 184 16 0.08333333
## 88 2021-05-30 new_deaths 237 220 17 0.07439825
## 89 2020-03-21 new_deaths 114 107 7 0.06334842
## 90 2021-06-11 new_deaths 326 306 20 0.06329114
## 91 2021-06-16 new_deaths 310 293 17 0.05638474
## 92 2021-06-15 new_deaths 336 319 17 0.05190840
## 93 2020-02-02 new_cases 1 557 556 1.99283154
## 94 2021-07-05 new_cases 11586 3575 8011 1.05679045
## 95 2021-07-04 new_cases 12813 4156 8657 1.02033119
## 96 2021-07-03 new_cases 14988 5887 9101 0.87195210
## 97 2021-06-10 new_cases 16732 12363 4369 0.30032652
## 98 2021-01-18 new_cases 138860 107646 31214 0.25325144
## 99 2021-01-19 new_cases 145017 176292 31275 0.19467242
## 100 2021-07-02 new_cases 16845 14183 2662 0.17158695
## 101 2021-06-20 new_cases 9238 7787 1451 0.17045521
## 102 2020-12-24 new_cases 222830 195402 27428 0.13116165
## 103 2021-06-01 new_cases 9691 8540 1151 0.12626844
## 104 2021-01-29 new_cases 156346 139722 16624 0.11229853
## 105 2020-12-26 new_cases 151877 169350 17473 0.10878911
## 106 2021-06-30 new_cases 17308 15526 1782 0.10854602
## 107 2021-06-28 new_cases 9695 8701 994 0.10806697
## 108 2021-06-09 new_cases 19405 21526 2121 0.10363783
## 109 2021-01-09 new_cases 249814 226455 23359 0.09809162
## 110 2021-01-30 new_cases 137320 150808 13488 0.09362506
## 111 2021-07-01 new_cases 18738 17149 1589 0.08855574
## 112 2021-06-08 new_cases 14357 15667 1310 0.08726352
## 113 2021-06-29 new_cases 16174 15051 1123 0.07192954
## 114 2021-06-06 new_cases 12103 11304 799 0.06827018
## 115 2020-07-14 new_cases 65688 61818 3870 0.06070303
## 116 2021-01-08 new_cases 295299 312357 17058 0.05614361
## 117 2021-05-24 new_cases 15656 14828 828 0.05432358
## 118 2021-05-31 new_cases 9193 9700 507 0.05367067
## 119 2021-05-03 new_cases 33241 31601 1640 0.05058450
## 120 2020-07-15 new_cases 70325 73939 3614 0.05010259
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 IN tot_deaths 3407157 3378244 28913 0.008522120
## 2 SC tot_deaths 2291589 2305862 14273 0.006209093
## 3 CA tot_deaths 14183041 14129523 53518 0.003780512
## 4 NC tot_deaths 3073917 3062861 11056 0.003603194
## 5 MS tot_deaths 1998075 1991323 6752 0.003384972
## 6 KY tot_deaths 1634463 1630052 4411 0.002702392
## 7 AL tot_deaths 2743934 2738028 5906 0.002154703
## 8 RI tot_deaths 749883 751479 1596 0.002126070
## 9 NM tot_deaths 1001515 999916 1599 0.001597857
## 10 CA tot_cases 865747767 837321729 28426038 0.033382123
## 11 AL tot_cases 132184325 131406619 777706 0.005900855
## 12 SC tot_cases 129358076 129977727 619651 0.004778754
## 13 RI tot_cases 32450790 32591078 140288 0.004313775
## 14 MI tot_cases 214132223 214386719 254496 0.001187793
## 15 MS tot_cases 77187328 77104046 83282 0.001079542
## 16 MS new_deaths 7432 7332 100 0.013546464
## 17 NM new_deaths 4382 4344 38 0.008709603
## 18 CA new_deaths 63517 62992 525 0.008299805
## 19 AL new_deaths 11454 11360 94 0.008240554
## 20 KY new_deaths 7285 7229 56 0.007716687
## 21 NC new_deaths 13517 13434 83 0.006159326
## 22 MI new_deaths 21076 20995 81 0.003850633
## 23 IN new_deaths 13914 13863 51 0.003672103
## 24 TX new_deaths 51507 51349 158 0.003072256
## 25 TN new_deaths 12611 12576 35 0.002779211
## 26 WA new_deaths 5954 5939 15 0.002522492
## 27 RI new_deaths 2736 2730 6 0.002195390
## 28 UT new_deaths 2371 2368 3 0.001266090
## 29 CA new_cases 3880232 3713944 166288 0.043793560
## 30 AL new_cases 555727 552325 3402 0.006140506
## 31 VI new_cases 3932 3916 16 0.004077472
## 32 MS new_cases 323003 321780 1223 0.003793524
## 33 LA new_cases 483605 482096 1509 0.003125191
## 34 NV new_cases 335771 334763 1008 0.003006559
## 35 FL new_cases 2344516 2337613 6903 0.002948659
## 36 WY new_cases 62592 62445 147 0.002351304
## 37 UT new_cases 416971 416110 861 0.002067026
## 38 KS new_cases 319154 318515 639 0.002004175
## 39 WA new_cases 453368 452483 885 0.001953964
## 40 AK new_cases 68595 68478 117 0.001707120
## 41 MI new_cases 1002081 1000375 1706 0.001703908
## 42 OR new_cases 209377 209035 342 0.001634752
## 43 NC new_cases 1015407 1014359 1048 0.001032631
##
##
##
## Raw file for cdcDaily:
## Rows: 33,540
## Columns: 15
## $ date <date> 2021-02-12, 2020-07-28, 2020-08-22, 2020-10-22, 2020-0~
## $ state <chr> "UT", "MP", "AR", "MP", "AS", "HI", "AK", "TX", "NYC", ~
## $ tot_cases <dbl> 359641, 40, 56199, 88, 0, 661, 71521, 1867163, 948436, ~
## $ conf_cases <dbl> 359641, 40, NA, 88, NA, NA, NA, NA, 782257, NA, 106, NA~
## $ prob_cases <dbl> 0, 0, NA, 0, NA, NA, NA, NA, 166179, NA, 0, NA, 0, 4003~
## $ new_cases <dbl> 1060, 0, 547, 0, 0, 8, 235, 24010, 394, 18811, 0, 0, 11~
## $ pnew_case <dbl> 0, 0, 0, 0, 0, 0, 0, 4196, 95, 3202, 0, 0, 0, 197, 0, 0~
## $ tot_deaths <dbl> 1785, 2, 674, 2, 0, 17, 377, 33124, 33203, 23357, 2, 0,~
## $ conf_death <dbl> 1729, 2, NA, 2, NA, NA, NA, NA, 28130, NA, 2, NA, NA, 1~
## $ prob_death <dbl> 56, 0, NA, 0, NA, NA, NA, NA, 5073, NA, 0, NA, NA, 350,~
## $ new_deaths <dbl> 11, 0, 11, 0, 0, 0, 0, 345, 6, 190, 0, 0, 7, 8, 5, 0, 1~
## $ pnew_death <dbl> 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, ~
## $ created_at <chr> "02/13/2021 02:50:08 PM", "07/29/2020 02:34:46 PM", "08~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", "Agree", NA, "Not agree"~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", "Agree", NA, "Not agree"~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 28
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 hosp_ped 662 597 65 0.10325655
## 2 2021-07-03 hosp_ped 638 597 41 0.06639676
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 523814 518483 5331 0.010229330
## 2 TN inp 558512 559654 1142 0.002042631
## 3 NM inp 137802 137991 189 0.001370593
## 4 NH hosp_ped 271 361 90 0.284810127
## 5 ME hosp_ped 452 509 57 0.118626431
## 6 KY hosp_ped 5518 5308 210 0.038795492
## 7 MA hosp_ped 5015 5201 186 0.036413469
## 8 AR hosp_ped 5977 5840 137 0.023186934
## 9 TN hosp_ped 7924 8102 178 0.022213902
## 10 DE hosp_ped 1647 1683 36 0.021621622
## 11 AL hosp_ped 7711 7555 156 0.020437574
## 12 WV hosp_ped 2226 2269 43 0.019132369
## 13 KS hosp_ped 1711 1679 32 0.018879056
## 14 NV hosp_ped 1999 2037 38 0.018830525
## 15 AZ hosp_ped 11435 11266 169 0.014889212
## 16 VA hosp_ped 6604 6513 91 0.013875124
## 17 IN hosp_ped 6913 6826 87 0.012664677
## 18 MS hosp_ped 3727 3686 41 0.011061648
## 19 MO hosp_ped 15406 15241 165 0.010767775
## 20 SC hosp_ped 2706 2679 27 0.010027855
## 21 PA hosp_ped 19857 20010 153 0.007675521
## 22 WA hosp_ped 4288 4263 25 0.005847269
## 23 NM hosp_ped 3125 3107 18 0.005776637
## 24 IA hosp_ped 2275 2287 12 0.005260851
## 25 CO hosp_ped 9355 9401 46 0.004905097
## 26 NJ hosp_ped 9108 9142 34 0.003726027
## 27 OH hosp_ped 25500 25406 94 0.003693081
## 28 IL hosp_ped 19711 19644 67 0.003404904
## 29 GA hosp_ped 21902 21973 71 0.003236467
## 30 MT hosp_ped 1022 1025 3 0.002931119
## 31 PR hosp_ped 11353 11380 27 0.002375401
## 32 CA hosp_ped 30719 30667 52 0.001694197
## 33 LA hosp_ped 3174 3179 5 0.001574059
## 34 TX hosp_ped 38680 38739 59 0.001524174
## 35 FL hosp_ped 54840 54921 81 0.001475934
## 36 HI hosp_ped 720 721 1 0.001387925
## 37 NC hosp_ped 10619 10606 13 0.001224971
## 38 AL hosp_adult 443621 439848 3773 0.008541330
## 39 TN hosp_adult 494022 494969 947 0.001915083
## 40 NM hosp_adult 112634 112842 208 0.001844986
## 41 ME hosp_adult 37173 37121 52 0.001399844
## 42 WV hosp_adult 126618 126444 174 0.001375157
## 43 KY hosp_adult 299353 299757 404 0.001348667
## 44 NH hosp_adult 39064 39014 50 0.001280771
## 45 CA hosp_adult 2422197 2425080 2883 0.001189534
##
##
##
## Raw file for cdcHosp:
## Rows: 27,682
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 7
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 15,113
## Columns: 69
## $ date <date> 2021-08-03, 2021-08-03, 2021-0~
## $ MMWR_week <dbl> 31, 31, 31, 31, 31, 31, 31, 31,~
## $ state <chr> "MH", "MN", "AL", "MD", "NC", "~
## $ Distributed <dbl> 51300, 6729450, 5167970, 880302~
## $ Distributed_Janssen <dbl> 10800, 349300, 281300, 440400, ~
## $ Distributed_Moderna <dbl> 40500, 2700980, 2377380, 345114~
## $ Distributed_Pfizer <dbl> 0, 3679170, 2509290, 4911480, 6~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 87823, 119324, 105400, 145608, ~
## $ Distributed_Per_100k_12Plus <dbl> 102839, 140840, 123432, 170506,~
## $ Distributed_Per_100k_18Plus <dbl> 112755, 155182, 135469, 186861,~
## $ Distributed_Per_100k_65Plus <dbl> 518339, 731158, 608113, 917559,~
## $ vxa <dbl> 36161, 6102310, 3634744, 723766~
## $ Administered_12Plus <dbl> 36132, 6082956, 3634417, 723724~
## $ Administered_18Plus <dbl> 36069, 5709218, 3515687, 675157~
## $ Administered_65Plus <dbl> 2101, 1599519, 1225637, 1686839~
## $ Administered_Janssen <dbl> 1206, 273277, 121830, 277559, 3~
## $ Administered_Moderna <dbl> 34955, 2359899, 1661529, 276459~
## $ Administered_Pfizer <dbl> 0, 3468298, 1851381, 4186640, 5~
## $ Administered_Unk_Manuf <dbl> 0, 836, 4, 8864, 457, 36, 565, ~
## $ Administered_Fed_LTC <dbl> 0, 176248, 90554, 195303, 22810~
## $ Administered_Fed_LTC_Residents <dbl> 0, 73383, 48060, 94356, 112427,~
## $ Administered_Fed_LTC_Staff <dbl> 0, 70268, 32757, 74670, 73173, ~
## $ Administered_Fed_LTC_Unk <dbl> 0, 32597, 9737, 26277, 42502, 2~
## $ Administered_Fed_LTC_Dose1 <dbl> 0, 107732, 55156, 115925, 13667~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 0, 43418, 28477, 52713, 63620, ~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 0, 41574, 20542, 43429, 42856, ~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 0, 22740, 6137, 19783, 30194, 1~
## $ Admin_Per_100k <dbl> 61906, 108204, 74130, 119716, 9~
## $ Admin_Per_100k_12Plus <dbl> 72432, 127309, 86805, 140178, 1~
## $ Admin_Per_100k_18Plus <dbl> 79278, 131656, 92157, 143315, 1~
## $ Admin_Per_100k_65Plus <dbl> 21229, 173789, 144220, 175823, ~
## $ Recip_Administered <dbl> 36214, 6130082, 3736050, 728051~
## $ Administered_Dose1_Recip <dbl> 19994, 3333060, 2154479, 392978~
## $ Administered_Dose1_Pop_Pct <dbl> 34.2, 59.1, 43.9, 65.0, 51.6, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 19973, 3321978, 2154149, 392930~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 40.0, 69.5, 51.4, 76.1, 60.1, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 19925, 3123896, 2075040, 366521~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 43.8, 72.0, 54.4, 77.8, 62.5, 6~
## $ Administered_Dose1_Recip_65Plus <dbl> 1157, 852313, 690242, 895212, 1~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 11.7, 92.6, 81.2, 93.3, 84.6, 9~
## $ vxc <dbl> 17307, 3040861, 1693504, 356886~
## $ vxcpoppct <dbl> 29.6, 53.9, 34.5, 59.0, 43.9, 4~
## $ Series_Complete_12Plus <dbl> 17298, 3032831, 1693466, 356870~
## $ Series_Complete_12PlusPop_Pct <dbl> 34.7, 63.5, 40.4, 69.1, 51.2, 5~
## $ vxcgte18 <dbl> 17275, 2858303, 1652134, 335011~
## $ vxcgte18pct <dbl> 38.0, 65.9, 43.3, 71.1, 53.6, 5~
## $ vxcgte65 <dbl> 1008, 801731, 591426, 844755, 1~
## $ vxcgte65pct <dbl> 10.2, 87.1, 69.6, 88.1, 77.2, 8~
## $ Series_Complete_Janssen <dbl> 1199, 274012, 124600, 269678, 3~
## $ Series_Complete_Moderna <dbl> 16097, 1116834, 746560, 1310927~
## $ Series_Complete_Pfizer <dbl> 11, 1649722, 822125, 1985705, 2~
## $ Series_Complete_Unk_Manuf <dbl> 0, 293, 219, 2558, 53, 18, 137,~
## $ Series_Complete_Janssen_12Plus <dbl> 1198, 273993, 124594, 269649, 3~
## $ Series_Complete_Moderna_12Plus <dbl> 16089, 1116822, 746549, 1310893~
## $ Series_Complete_Pfizer_12Plus <dbl> 11, 1641723, 822104, 1985603, 2~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 0, 293, 219, 2558, 53, 18, 137,~
## $ Series_Complete_Janssen_18Plus <dbl> 1195, 273518, 124535, 269534, 3~
## $ Series_Complete_Moderna_18Plus <dbl> 16071, 1114783, 746285, 1310577~
## $ Series_Complete_Pfizer_18Plus <dbl> 9, 1469720, 781095, 1767515, 23~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 0, 282, 219, 2485, 52, 18, 120,~
## $ Series_Complete_Janssen_65Plus <dbl> 65, 46101, 32206, 50551, 42106,~
## $ Series_Complete_Moderna_65Plus <dbl> 943, 338638, 312228, 402682, 65~
## $ Series_Complete_Pfizer_65Plus <dbl> 0, 416927, 246854, 390774, 6574~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 0, 65, 138, 748, 35, 11, 54, 0,~
## $ Series_Complete_FedLTC <dbl> 0, 68296, 35927, 78415, 90579, ~
## $ Series_Complete_FedLTC_Residents <dbl> 0, 29784, 19890, 41147, 47820, ~
## $ Series_Complete_FedLTC_Staff <dbl> 0, 28507, 12335, 30545, 29596, ~
## $ Series_Complete_FedLTC_Unknown <dbl> 0, 10005, 3702, 6723, 13163, 14~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.31e+9 1.66e+8 3.51e+7 605510 32981
## 2 after 8.27e+9 1.65e+8 3.49e+7 602741 28509
## 3 pctchg 4.40e-3 3.97e-3 4.59e-3 0.00457 0.136
##
##
## Processed for cdcDaily:
## Rows: 28,509
## Columns: 6
## $ date <date> 2021-02-12, 2020-08-22, 2020-06-05, 2021-07-27, 2021-01-06~
## $ state <chr> "UT", "AR", "HI", "AK", "TX", "TX", "GA", "MA", "OK", "AK",~
## $ tot_cases <dbl> 359641, 56199, 661, 71521, 1867163, 1236648, 493, 662699, 2~
## $ tot_deaths <dbl> 1785, 674, 17, 377, 33124, 23357, 13, 17427, 102, 17, 1331,~
## $ new_cases <dbl> 1060, 547, 8, 235, 24010, 18811, 115, 1598, 96, 29, 89, 870~
## $ new_deaths <dbl> 11, 11, 0, 0, 345, 190, 7, 8, 5, 0, 1, 15, 2, 1, 0, 0, 34, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.78e+7 2.19e+7 471723 27682
## 2 after 2.77e+7 2.18e+7 459822 26679
## 3 pctchg 5.58e-3 5.57e-3 0.0252 0.0362
##
##
## Processed for cdcHosp:
## Rows: 26,679
## Columns: 5
## $ date <date> 2020-07-22, 2020-07-20, 2020-07-19, 2020-07-18, 2020-07-18~
## $ state <chr> "IA", "IA", "ND", "IA", "ND", "TX", "OK", "CT", "ND", "NM",~
## $ inp <dbl> 0, 1, 46, 10, 33, 12003, 678, 215, 16, 119, 51, 19, 250, 14~
## $ hosp_adult <dbl> 0, 1, NA, 10, NA, 7999, 566, 115, NA, NA, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> 0, 0, NA, 0, NA, 194, 9, 0, NA, NA, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 8.40e+10 3.47e+10 311274. 1.13e+10 575411. 3.38e+10 384223.
## 2 after 4.00e+10 1.68e+10 263386. 5.47e+ 9 524476. 1.63e+10 329394.
## 3 pctchg 5.24e- 1 5.16e- 1 0.154 5.16e- 1 0.0885 5.16e- 1 0.143
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 11,883
## Columns: 9
## $ date <date> 2021-08-03, 2021-08-03, 2021-08-03, 2021-08-03, 2021-08-0~
## $ state <chr> "MN", "AL", "MD", "NC", "SD", "MO", "ND", "NE", "WY", "AZ"~
## $ vxa <dbl> 6102310, 3634744, 7237660, 9776317, 854287, 5503756, 66497~
## $ vxc <dbl> 3040861, 1693504, 3568868, 4606310, 416490, 2549091, 30623~
## $ vxcpoppct <dbl> 53.9, 34.5, 59.0, 43.9, 47.1, 41.5, 40.2, 49.7, 36.7, 45.4~
## $ vxcgte65 <dbl> 801731, 591426, 844755, 1352070, 131441, 792717, 89800, 26~
## $ vxcgte65pct <dbl> 87.1, 69.6, 88.1, 77.2, 86.5, 74.6, 74.9, 85.7, 73.6, 74.5~
## $ vxcgte18 <dbl> 2858303, 1652134, 3350111, 4390894, 400144, 2446269, 29565~
## $ vxcgte18pct <dbl> 65.9, 43.3, 71.1, 53.6, 59.9, 51.3, 50.8, 62.6, 46.2, 55.7~
##
## Integrated per capita data file:
## Rows: 28,722
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_210804)The raw hospital data is explored for admissions with confirmed/suspected coronavirus:
cdcHospAdmit_210804 <- cdc_daily_210804$dfRaw$cdcHosp %>%
select(state, date, ends_with("confirmed"), ends_with("suspected")) %>%
filter(state %in% c(state.abb, "DC")) %>%
arrange(date, state) %>%
pivot_longer(-c(state, date)) %>%
mutate(name=stringr::str_replace(name, pattern="previous_day_admission_", replacement=""),
name=stringr::str_replace(name, pattern="_covid", replacement="")
) %>%
left_join(getStateData(keepVars=c("state", "pop")), by="state") %>%
mutate(vpm=1000000*value/pop) %>%
group_by(state, name) %>%
mutate(vpm7=zoo::rollmean(vpm, k=7, fill=NA)) %>%
ungroup()
cdcHospAdmit_210804 %>%
filter(!is.na(vpm7), state %in% state.abb) %>%
mutate(div=as.character(state.division)[match(state, state.abb)]) %>%
group_by(div, date, name) %>%
summarize(wt_vpm7=sum(pop*vpm7)/sum(pop), tot_pop=sum(pop), .groups="drop") %>%
ggplot(aes(x=date, y=wt_vpm7)) +
geom_col(aes(fill=name), position="stack") +
facet_wrap(~div) +
scale_fill_discrete("Metric") +
labs(x=NULL,
y="Newly admitted for COVID per million (rolling 7-day)",
title="Hospital admissions for COVID",
subtitle="All metrics divided by total population (all ages) for states reporting"
)
Hospital coverage data became robust about a year ago. The overwhelming majority of admissions are adult, split between confirmed and suspected cases. Next steps are to explore changes in admissions by age groups:
hospAge_210804 <- cdc_daily_210804$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_210804## # A tibble: 498,276 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 0-19 <NA>
## 2 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 20-29 <NA>
## 3 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 30-39 <NA>
## 4 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 40-49 <NA>
## 5 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 50-59 <NA>
## 6 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 60-69 <NA>
## 7 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 70-79 <NA>
## 8 PR 2020-07-27 previous_day_admission_~ NA confirm~ adult 80+ <NA>
## 9 PR 2020-07-27 previous_day_admission_~ NA suspect~ adult 0-19 <NA>
## 10 PR 2020-07-27 previous_day_admission_~ NA suspect~ adult 20-29 <NA>
## # ... with 498,266 more rows# Plot for overall trends by age group
p1 <- hospAge_210804 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Jul 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Line plots by age group
p1 +
geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
# Stacked bar plots by age group
p1 +
geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Proportions by age group
p1 +
geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_210804 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Jul 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
Next steps are to explore alignment of the hospitalization and case/death curves. Michigan having had a distinct spring peak is used as an example:
allHosp_210804 <- hospAge_210804 %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
ungroup() %>%
left_join(getStateData(keepVars=c("state", "pop"))) %>%
mutate(vpm7=1000000*value7/pop)## Joining, by = "state"allHosp_210804## # A tibble: 83,046 x 7
## date state ageBucket value value7 pop vpm7
## <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-01 AL 0-19 NA NA 4903185 NA
## 2 2020-01-01 AL 20-59 NA NA 4903185 NA
## 3 2020-01-01 AL 60+ NA NA 4903185 NA
## 4 2020-01-01 HI 0-19 NA NA 1415872 NA
## 5 2020-01-01 HI 20-59 NA NA 1415872 NA
## 6 2020-01-01 HI 60+ NA NA 1415872 NA
## 7 2020-01-01 IN 0-19 NA NA 6732219 NA
## 8 2020-01-01 IN 20-59 NA NA 6732219 NA
## 9 2020-01-01 IN 60+ NA NA 6732219 NA
## 10 2020-01-01 LA 0-19 NA NA 4648794 NA
## # ... with 83,036 more rowsallCaseDeath_210804 <- cdc_daily_210804$dfPerCapita %>%
select(state, date, new_cases, new_deaths, vxa, vxc, cpm7, dpm7, vxapm7, vxcpm7) %>%
pivot_longer(-c(state, date))
allCaseDeath_210804## # A tibble: 229,776 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 new_cases NA
## 2 AL 2020-01-01 new_deaths NA
## 3 AL 2020-01-01 vxa NA
## 4 AL 2020-01-01 vxc NA
## 5 AL 2020-01-01 cpm7 NA
## 6 AL 2020-01-01 dpm7 NA
## 7 AL 2020-01-01 vxapm7 NA
## 8 AL 2020-01-01 vxcpm7 NA
## 9 HI 2020-01-01 new_cases NA
## 10 HI 2020-01-01 new_deaths NA
## # ... with 229,766 more rowsallPivot_210804 <- allHosp_210804 %>%
select(state, date, name=ageBucket, value=vpm7) %>%
bind_rows(allCaseDeath_210804) %>%
checkUniqueRows(uniqueBy=c("state", "date", "name"))##
## *** File has been checked for uniqueness by: state date nameallPivot_210804## # A tibble: 312,822 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 312,812 more rows# Plot Michigan data
typeMapper <- c("cases"="1. Cases per million per day (rolling 7 mean)",
"deaths"="2. Deaths per million per day (rolling 7 mean)",
"hosp"="3. Admitted to hospital per million per day (rolling 7 mean)",
"vax"="4. Vaccinated per capita (administered, completed)"
)
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType != "notuse") %>%
ggplot(aes(x=date, y=value)) +
geom_line(data=~filter(., plotType=="cases"), color="blue", size=1) +
geom_line(data=~filter(., plotType=="deaths"), color="red", size=1) +
geom_line(data=~filter(., plotType=="vax"), aes(color=name, group=name, y=value/1000000)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name), position="stack") +
scale_color_discrete("Vaccine\nMetric") +
scale_fill_discrete("Hospitalized\nby Age") +
facet_wrap(~typeMapper[plotType], scales="free_y") +
labs(x=NULL, y=NULL) +
lims(y=c(0, NA))
hospCase <- 10
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType %in% c("cases", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospCase*value), position="stack") +
geom_line(data=~filter(., plotType=="cases"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Cases per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospCase,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title="Alignment of Michigan cases and hospitalizations data",
subtitle=paste0("Ratio of ",
hospCase,
":1 applied (cases are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
hospDeath <- 0.2
allPivot_210804 %>%
filter(state=="MI", !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
) %>%
filter(plotType %in% c("deaths", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospDeath*value), position="stack") +
geom_line(data=~filter(., plotType=="deaths"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Deaths per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospDeath,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title="Alignment of Michigan deaths and hospitalizations data",
subtitle=paste0("Ratio of ",
hospDeath,
":1 applied (deaths are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
At a glance, the Michigan data appear reasonably well aligned. Hospital admissions run at ~10% of confirmed cases with a small delay. Deaths run at ~20% of hospital admissions in the mid-winter wave and ~10% of admissions in the spring wave. These are consistent with estimates of ~1.5% CFR and ~5 cases per diagnosed cases (fatality rate ~0.3%).
Next steps are to convert this process to functional form, automate selection of scaling parameters, and explore other states of interest:
# Function to create case-hospital-death file
makeCaseHospDeath <- function(dfHosp, dfCaseDeath) {
# FUNCTION ARGUMENTS:
# dfHosp: the tibble or data.frame containing the hospital data by date-state
# dfCaseDeath: the tibble or data.frame containing the case and death data by date-state
allHosp <- dfHosp %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
ungroup() %>%
left_join(getStateData(keepVars=c("state", "pop"))) %>%
mutate(vpm7=1000000*value7/pop)
allCaseDeath <- dfCaseDeath %>%
select(state, date, new_cases, new_deaths, vxa, vxc, cpm7, dpm7, vxapm7, vxcpm7) %>%
pivot_longer(-c(state, date))
allPivot <- allHosp %>%
select(state, date, name=ageBucket, value=vpm7) %>%
bind_rows(allCaseDeath) %>%
checkUniqueRows(uniqueBy=c("state", "date", "name"))
allPivot
}
alignCaseHospDeath <- function(dfPivot=NULL,
dfHosp=NULL,
dfCaseDeath=NULL,
typeMapper=c("cases"="1. Cases per million per day (rolling 7 mean)",
"deaths"="2. Deaths per million per day (rolling 7 mean)",
"hosp"="3. Admitted to hospital per million per day (rolling 7 mean)",
"vax"="4. Vaccinated per capita (administered, completed)"
),
keyState="MI",
hospCaseScalar=10,
hospDeathScalar=0.2,
returnPlots=FALSE
)
{
# FUNCTION ARGUMENTS:
# dfPivot: the tibble or data.frame containing integrated case-hospital-death data
# (if NULL, build from dfHosp and dfCaseDeath)
# dfHosp: the tibble or data.frame containing the hospital data by date-state
# dfCaseDeath: the tibble or data.frame containing the case and death data by date-state
# typeMapper: mapping file for labelling facets
# keyState: the state to explore
# hospCaseScalar: the scalar to be applied for placing cases and hospitalizations on the same plot
# hospDeathScalar: the scalar to be applied for placing hospitalizations and deaths on the same plot
# Create the pivoted data if it was not passed
if (is.null(dfPivot)) {
if (is.null(dfHosp) | is.null(dfCaseDeath)) stop("\nMust pass dfPivot OR both of dfHosp and dfCaseDeath\n")
dfPivot <- makeCaseHospDeath(dfHosp=dfHosp, dfCaseDeath=dfCaseDeath)
}
# Create the plotting data
plotData <- dfPivot %>%
filter(state==keyState, !is.na(value)) %>%
mutate(plotType=case_when(name %in% c("0-19", "20-59", "60+") ~ "hosp",
name %in% c("vxapm7", "vxcpm7") ~ "vax",
name=="cpm7" ~ "cases",
name=="dpm7" ~ "deaths",
TRUE ~ "notuse"
)
)
# Create overall plot
p1 <- plotData %>%
filter(plotType != "notuse") %>%
ggplot(aes(x=date, y=value)) +
geom_line(data=~filter(., plotType=="cases"), color="blue", size=1) +
geom_line(data=~filter(., plotType=="deaths"), color="red", size=1) +
geom_line(data=~filter(., plotType=="vax"), aes(color=name, group=name, y=value/1000000)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name), position="stack") +
scale_color_discrete("Vaccine\nMetric") +
scale_fill_discrete("Hospitalized\nby Age") +
facet_wrap(~typeMapper[plotType], scales="free_y") +
labs(x=NULL, y=NULL, title=paste0("Key coronavirus metrics for state: ", keyState)) +
lims(y=c(0, NA))
print(p1)
p2 <- plotData %>%
filter(plotType %in% c("cases", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospCaseScalar*value), position="stack") +
geom_line(data=~filter(., plotType=="cases"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Cases per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospCaseScalar,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title=paste0("Alignment of ", keyState, " cases and hospitalizations data"),
subtitle=paste0("Ratio of ",
hospCaseScalar,
":1 applied (cases are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
print(p2)
p3 <- plotData %>%
filter(plotType %in% c("deaths", "hosp")) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., plotType=="hosp"), aes(fill=name, y=hospDeathScalar*value), position="stack") +
geom_line(data=~filter(., plotType=="deaths"), color="black", size=1) +
scale_fill_discrete("Hospital Admssions by Age") +
scale_y_continuous("Deaths per million (rolling 7 mean per day)",
sec.axis = sec_axis(~ . / hospDeathScalar,
name = "Hospital admissions per million (rolling 7 mean per day"
)
) +
labs(x=NULL,
title=paste0("Alignment of ", keyState, " deaths and hospitalizations data"),
subtitle=paste0("Ratio of ",
hospDeathScalar,
":1 applied (deaths are black line, hospital admissions are stacked bar)"
)
) +
theme(legend.position="bottom")
print(p3)
if(isTRUE(returnPlots)) list(p1=p1, p2=p2, p3=p3)
}The function is tested for MI, FL, and TX:
dfPivot_210804 <- makeCaseHospDeath(dfHosp=hospAge_210804, dfCaseDeath=cdc_daily_210804$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_210804## # A tibble: 312,822 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 312,812 more rowsmiAlign <- alignCaseHospDeath(dfPivot_210804, keyState="MI", returnPlots=TRUE)
flAlign <- alignCaseHospDeath(dfPivot_210804, keyState="FL", returnPlots=TRUE, hospDeathScalar=0.14)
txAlign <- alignCaseHospDeath(dfPivot_210804, keyState="TX", returnPlots=TRUE, hospDeathScalar=0.14)
# Key plots on a single page
gridExtra::grid.arrange(miAlign$p1, flAlign$p1, txAlign$p1, nrow=2)
gridExtra::grid.arrange(miAlign$p2, flAlign$p2, txAlign$p2, nrow=1)
gridExtra::grid.arrange(miAlign$p3, flAlign$p3, txAlign$p3, nrow=1)
Next, an implied CFR (deaths per case) rate is calculated for inclusion:
# Filter to single-state data
miData <- dfPivot_210804 %>%
filter(state=="MI", name %in% c("cpm7", "dpm7"), !is.na(value))
# Plot core metrics
miData %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~c("cpm7"="Cases per million", "dpm7"="Deaths per million")[name], scales="free_y") +
labs(x=NULL, y="Value per million (rolling 7-day mean)", title="Coronavirus burden in state: MI") +
theme(legend.position="none")
# Create correlation for assigned lag/lead and variables in a data frame
lagCorrCheck <- function(df, lagLead=0, varFix="dpm7", varMove="cpm7") {
df %>%
mutate(lagVar=if(lagLead >= 0) lag(get(varMove), lagLead) else lead(get(varMove), abs(lagLead))) %>%
filter(!is.na(lagVar)) %>%
summarize(correl=cor(lagVar, get(varFix))) %>%
pull(correl)
}
miCorrDF <- miData %>%
select(date, name, value) %>%
pivot_wider(date)
miCorrDF## # A tibble: 553 x 3
## date cpm7 dpm7
## <date> <dbl> <dbl>
## 1 2020-01-25 0 0
## 2 2020-01-26 0 0
## 3 2020-01-27 0 0
## 4 2020-01-28 0 0
## 5 2020-01-29 0 0
## 6 2020-01-30 0 0
## 7 2020-01-31 0 0
## 8 2020-02-01 0 0
## 9 2020-02-02 0 0
## 10 2020-02-03 0 0
## # ... with 543 more rows# Assessing best lag/lead for full dataset
lagLeads=-10:40
miRhoFull <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads, FUN=function(x) lagCorrCheck(miCorrDF, lagLead=x))
)
bestFull <- miRhoFull %>% filter(rho==max(rho))
miRhoFull %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestFull, aes(yintercept=rho), lty=2) +
geom_vline(data=bestFull, aes(xintercept=lagLead), lty=2) +
labs(x="Lag or lead of cases",
y="Correlation to deaths",
title="All Michigan case and death data",
subtitle=paste0("Best correlation ",
round(bestFull$rho, 3),
" obtained at lag/lead of: ",
bestFull$lagLead
)
)
# Assessing best lag/lead for August 2020 and beyond
lagLeads=-10:40
miRhoLate <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads,
FUN=function(x) lagCorrCheck(miCorrDF %>% filter(date >= "2020-08-01"),
lagLead=x
)
)
)
bestLate <- miRhoLate %>% filter(rho==max(rho))
miRhoLate %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestLate, aes(yintercept=rho), lty=2) +
geom_vline(data=bestLate, aes(xintercept=lagLead), lty=2) +
labs(x="Lag or lead of cases",
y="Correlation to deaths",
title="Michigan case and death data (August 2020 to present)",
subtitle=paste0("Best correlation ",
round(bestLate$rho, 3),
" obtained at lag/lead of: ",
bestLate$lagLead
)
)
miCorrDF %>%
mutate(lag20=lag(cpm7, 20)) %>%
filter(!is.na(lag20), lag20 > 0) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lag20), color="navy", lty=2) +
geom_line(aes(y=8000*pmin(0.1, dpm7/lag20)), color="red") +
scale_y_continuous("Cases per million (rolling 7 mean per day) - actual and 20-day lag",
sec.axis = sec_axis(~ . / 8000,
name = "Implied CFR (capped at 10%)"
)
) +
labs(x=NULL, title="Implied case fatality rate using 20-day lag in Michigan")
miCorrDF %>%
mutate(lag20=lag(cpm7, 20)) %>%
filter(!is.na(lag20)) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lag20), color="navy", lty=2) +
geom_line(aes(y=50*dpm7), color="red") +
labs(x=NULL,
y="Per million (7-day rolling mean)\nCases, Lag-20 cases, and 50*Deaths",
title="Burden metrics for Michigan"
)
Michigan appears to fairly consistently have a lag of 2-3 weeks between cases and deaths. The CFR during spikes has consistently declined from over 10% to around 2% to around 1%. The estimated CFR outside of spikes appears noisy. Next steps are to convert to functional form and run for other states:
# Create correlation for assigned lag/lead and variables in a data frame
lagCorrCheck <- function(df, lagLead=0, varFix="dpm7", varMove="cpm7") {
df %>%
mutate(lagVar=if(lagLead >= 0) lag(get(varMove), lagLead) else lead(get(varMove), abs(lagLead))) %>%
filter(!is.na(lagVar)) %>%
summarize(correl=cor(lagVar, get(varFix))) %>%
pull(correl)
}
findCorrAlign <- function(df,
keyState,
varFix="dpm7",
varMove="cpm7",
lagLeads=-10:40,
minDate=NULL,
maxDate=NULL,
varMapper=c("cpm7"="Cases per million", "dpm7"="Deaths per million"),
yLab="Value per million (rolling 7-day mean)",
printPlots=TRUE,
returnPlots=FALSE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS
# df: pivoted data frame with state-date-name-value
# keyState: state to include
# varFix: metric to be held constant
# varMove: metric to be lagged/led
# lagLeads: lags and leads for the variable that moves
# minDate: minimum date for lag/lead (NULL means data-driven)
# maxDate: maximum date for lag/lead (NULL means data-driven)
# varMapper: mapping file for varFix and varMove to descriptive labels
# yLab: label for the y-axis in the first plot
# printPlots: boolean, should the plots be printed?
# returnPlots: boolean, should the plots be returned?
# returnData: boolean, should the data frames be returned as a list?
# Set minDate and maxDate to the actual minmax if passed as NULL
if (is.null(minDate)) minDate <- df %>% summarize(date=min(date)) %>% pull(date)
if (is.null(maxDate)) maxDate <- df %>% summarize(date=max(date)) %>% pull(date)
# Filter to relevant data
df <- df %>%
filter(state %in% all_of(keyState), name %in% all_of(c(varFix, varMove)), !is.na(value))
# Plot core metrics for requested states
p1 <- df %>%
ggplot(aes(x=date, y=value)) +
geom_line(aes(group=name, color=name)) +
facet_wrap(~varMapper[name], scales="free_y") +
labs(x=NULL, y=yLab, title=paste0("Metrics by state for: ", paste0(keyState, collapse=", "))) +
theme(legend.position="none")
if(isTRUE(printPlots)) print(p1)
# Create dataset for correlations
dfCorr <- df %>%
select(date, name, value) %>%
pivot_wider(date)
# Find correlation by lag/lead for dataset
dfRho <- tibble::tibble(lagLead=lagLeads,
rho=sapply(lagLeads,
FUN=function(x) {
lagCorrCheck(dfCorr %>% filter(date >= minDate, date <= maxDate),
lagLead=x
)
}
)
)
# Find best correlation and lag/lead
bestRho <- dfRho %>%
filter(rho==max(rho))
# Plot correlations by lag/lead
p2 <- dfRho %>%
ggplot(aes(x=lagLead, y=rho)) +
geom_point() +
geom_hline(data=bestRho, aes(yintercept=rho), lty=2) +
geom_vline(data=bestRho, aes(xintercept=lagLead), lty=2) +
labs(x=paste0("Lag or lead of ", varMapper[varMove]),
y=paste0("Correlation to ", varMapper[varFix]),
title=paste0("Correlations by lag/lead for state: ", keyState),
subtitle=paste0("Best correlation ",
round(bestRho$rho, 3),
" obtained at lag/lead of: ",
bestRho$lagLead
)
)
if(isTRUE(printPlots)) print(p2)
if (isTRUE(returnData) | isTRUE(returnPlots)) {
list(dfRho=if(isTRUE(returnData)) dfRho else NULL,
bestRho=if(isTRUE(returnData)) bestRho else NULL,
dfCorr=if(isTRUE(returnData)) dfCorr else NULL,
p1=if(isTRUE(returnPlots)) p1 else NULL,
p2=if(isTRUE(returnPlots)) p2 else NULL
)
}
}
findCorrAlign(dfPivot_210804, keyState="MI")
findCorrAlign(dfPivot_210804, keyState="MI", minDate="2020-08-01", returnData=TRUE)
## $dfRho
## # A tibble: 51 x 2
## lagLead rho
## <int> <dbl>
## 1 -10 0.320
## 2 -9 0.348
## 3 -8 0.376
## 4 -7 0.404
## 5 -6 0.432
## 6 -5 0.460
## 7 -4 0.488
## 8 -3 0.515
## 9 -2 0.543
## 10 -1 0.570
## # ... with 41 more rows
##
## $bestRho
## # A tibble: 1 x 2
## lagLead rho
## <int> <dbl>
## 1 20 0.880
##
## $dfCorr
## # A tibble: 553 x 3
## date cpm7 dpm7
## <date> <dbl> <dbl>
## 1 2020-01-25 0 0
## 2 2020-01-26 0 0
## 3 2020-01-27 0 0
## 4 2020-01-28 0 0
## 5 2020-01-29 0 0
## 6 2020-01-30 0 0
## 7 2020-01-31 0 0
## 8 2020-02-01 0 0
## 9 2020-02-02 0 0
## 10 2020-02-03 0 0
## # ... with 543 more rows
##
## $p1
## NULL
##
## $p2
## NULLfindCorrAlign(dfPivot_210804, keyState="FL")
findCorrAlign(dfPivot_210804, keyState="TX", minDate="2020-10-01")
Next steps are to create functional form for calculating IFR and aligning plots:
plotCFRLag <- function(lst,
lagUse=NULL,
scaleUse=NULL,
cfrCap=0.06,
multDeath=50,
mainTitle="Coronavirus data for selected geography: ",
printPlots=TRUE,
returnPlots=FALSE
) {
# FUNCTION ARGUMENTS:
# lst: data frame with date-cpm7-dpm7 OR list with both dfCorr and bestRho
# lagUse: the lag to use (if NULL, use the value in bestRho$lagLead)
# scaleUse: scalar for secondary y-axis (NULL means calculate from data)
# cfrCap: the cap for all values of CFR
# multDeath: multiplier for death data in plot 2
# mainTitle: main title for plots
# printPlots: boolean, should the plots be printed?
# returnPlots: boolean, should the plots be returned?
# Create dfCorr and lagUse
if ("list" %in% class(lst)) {
dfCorr <- lst[["dfCorr"]]
if (is.null(lagUse)) lagUse <- lst[["bestRho"]]$lagLead
} else {
dfCorr <- lst
}
# Check that dfCorr is a data frame with date-cpm7-dpm7 and lagUse is not NULL
if (!("data.frame" %in% class(dfCorr))) stop("\nMust have a data frame for lst/dfCorr\n")
if (!(all(c("date", "cpm7", "dpm7") %in% names(dfCorr)))) stop("\ndfCorr must have date-cpm7-dpm7\n")
if (is.null(lagUse)) stop("\nMust have a value for lagUse\n")
# Create scaleUse if not passed
if (is.null(scaleUse)) scaleUse <- 500*ceiling(max(dfCorr$cpm7)/cfrCap/500)
# Create plot of CFR by date, showing lagged cases
basePlot <- dfCorr %>%
mutate(lagData=if(lagUse >= 0) lag(cpm7, lagUse) else lead(cpm7, -lagUse)) %>%
filter(!is.na(lagData), lagData > 0) %>%
ggplot(aes(x=date)) +
geom_line(aes(y=cpm7), color="navy") +
geom_line(aes(y=lagData), color="navy", lty=2)
p1 <- basePlot +
geom_line(aes(y=scaleUse*pmin(cfrCap, dpm7/lagData)), color="red") +
scale_y_continuous(paste0("Cases per million\n(rolling 7-day mean)"),
sec.axis = sec_axis(~ . / scaleUse,
name = paste0("Implied CFR (capped at ",
round(100*cfrCap, 1),
"%)"
)
)
) +
labs(x=NULL,
title=mainTitle,
subtitle=paste0("Red line (right axis) is implied fatality rate\n",
"Blue line is cases with and without ",
abs(lagUse),
"-day ",
if(lagUse > 0) "lag" else "lead"
)
)
if (isTRUE(printPlots)) print(p1)
# Apply a CFR to the data and show alignment
p2 <- basePlot +
geom_line(aes(y=multDeath*dpm7), color="red") +
labs(x=NULL,
y="Per million (7-day rolling mean)",
title=mainTitle,
subtitle=paste0("Red line is ",
multDeath,
"*deaths\n",
"Blue line is cases with and without ",
abs(lagUse),
"-day ",
if(lagUse > 0) "lag" else "lead"
)
)
if (isTRUE(printPlots))print(p2)
if (isTRUE(returnPlots)) list(p1=p1, p2=p2)
}
# Run for Michigan, Florida, California
findCorrAlign(dfPivot_210804, keyState="MI", minDate="2020-08-01", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "MI"))
findCorrAlign(dfPivot_210804, keyState="FL", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "FL"), multDeath=100)
findCorrAlign(dfPivot_210804, keyState="CA", returnData=TRUE) %>%
plotCFRLag(mainTitle=paste0(formals(plotCFRLag)$mainTitle, "CA"), multDeath=70)
Functions have been updated so that plots can be returned and/or printed. A new function allows for creating all plots for state on a single page:
# Function to plot all states on the same page
onePageCFRPlot <- function(df, keyState, multDeath=100, cfrCap=0.06, ...) {
# FUNCTION ARGUMENTS:
# df: the data frame containing state-date-name-value
# keyState: the key state to be analyzed
# multDeath: multiplier for death in the death/lagged cases chart of plotCFRLag()
# ...: other arguments to be passed to findCorrAlign()
# Find the correlations data
corrData <- findCorrAlign(df,
keyState=keyState,
yLab="Value per million\n(rolling 7-day mean)",
printPlots=FALSE,
returnPlots=TRUE,
returnData=TRUE,
...
)
# Find CFR
cfrData <- plotCFRLag(corrData,
cfrCap=cfrCap,
multDeath=multDeath,
mainTitle=paste0(formals(plotCFRLag)$mainTitle, keyState),
printPlots=FALSE,
returnPlots=TRUE
)
# Create single-page summary
gridExtra::grid.arrange(corrData$p1, corrData$p2, cfrData$p1, cfrData$p2, nrow=2)
}
# Run for Michigan, Florida, California, Texas, New York, South Dakota
onePageCFRPlot(dfPivot_210804, keyState="MI", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210804, keyState="FL", multDeath=100)
onePageCFRPlot(dfPivot_210804, keyState="CA", multDeath=70)
onePageCFRPlot(dfPivot_210804, keyState="TX", multDeath=70)
onePageCFRPlot(dfPivot_210804, keyState="NY", multDeath=10, cfrCap=0.1)
onePageCFRPlot(dfPivot_210804, keyState="SD", multDeath=70)
Summaries are created for vaccination status by age cohort and state:
cdc_daily_210815 <- readFromRDS("cdc_daily_210815")
# Example using hard coding and wide data
cdc_daily_210815$dfRaw$vax %>%
filter(date==max(date), state %in% c(state.abb, "DC")) %>%
select(state, contains("Administered_Dose1")) %>%
ggplot(aes(x=fct_reorder(state, Administered_Dose1_Recip_65PlusPop_Pct))) +
geom_col(aes(fill="65+", y=Administered_Dose1_Recip_65PlusPop_Pct)) +
geom_col(aes(y=Administered_Dose1_Recip_18PlusPop_Pct, fill="18+")) +
geom_col(aes(y=Administered_Dose1_Pop_Pct, fill="All")) +
geom_text(aes(y=Administered_Dose1_Recip_65PlusPop_Pct,
label=paste0(Administered_Dose1_Recip_65PlusPop_Pct, "% (", state, ")")
),
hjust=0,
size=3
) +
geom_text(aes(y=Administered_Dose1_Recip_18PlusPop_Pct+0.5,
label=paste0(Administered_Dose1_Recip_18PlusPop_Pct, "%")
),
hjust=0,
size=3
) +
coord_flip() +
labs(x=NULL,
y="% Fully Vaccinated",
title="First-dose vaccinated by age cohort and state (mid-Aug 2020)",
subtitle="Black: all population, Yellow: 18+, Blue: 65+"
) +
scale_fill_manual("Cohort",
breaks=c("All", "18+", "65+"),
values=c("65+"="lightblue", "18+"="yellow", "All"="black" )
) +
geom_text(aes(y=Administered_Dose1_Pop_Pct+0.5,
label=paste0(Administered_Dose1_Pop_Pct, "%")
),
hjust=0,
size=3
) +
theme(legend.position="bottom")
# Function for reproducibility
tempStackPlot <- function(df,
yVars,
xVar="state",
yLab="",
plotTitle="",
colorVector=c("lightblue", "grey", "orange", "black")
) {
# FUNCTION ARGUMENTS:
# df: data frame or tibble
# yVars: named vector with c("variable"="name")
# xVar: the x variable
# yLab: the y-axis label for the plot
# plotTitle: the title for the plot
# colorVector: colors to use for filled bars (sequentially, can have more, but not less, than length(yVars))
colorVector <- colorVector[1:length(yVars)]
names(colorVector) <- names(yVars)
p1 <- df %>%
select(all_of(xVar), all_of(names(yVars))) %>%
pivot_longer(-c(all_of(xVar))) %>%
ggplot(aes(x=fct_reorder(get(xVar[1]), value, max))) +
coord_flip() +
labs(x=NULL, y=yLab, title=plotTitle) +
geom_col(aes(y=value, fill=name), position="identity") +
geom_text(aes(y=value+0.5,
label=paste0(value, "%", ifelse(name==names(yVars)[1], paste0(" (", state, ")"), ""))
),
hjust=0,
size=3
) +
scale_fill_manual("Cohort",
breaks=rev(names(yVars)),
labels=rev(unname(yVars)),
values=colorVector
) +
theme(legend.position="bottom")
p1
}
# Run for fully vaccinated
tempStackPlot(cdc_daily_210815$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle="Fully vaccinated by age cohort and state (mid-Aug 2020)"
)
# Run for first dose
tempStackPlot(cdc_daily_210815$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle="First-dose vaccinated by age cohort and state (mid-Aug 2020)"
)
The plotting is extended to show the evolution of vaccination over time:
# Create data
cdcVaxGrowth <- cdc_daily_210815$dfRaw$vax %>%
filter(date %in% c(as.Date(max(date)-lubridate::dmonths(c(0, 2, 4)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for fuly vaccinated
p1 <- cdcVaxGrowth %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p1 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
Similar plots are created for the evolution of cases per thousand and deaths per milion:
# Updated function for reproducibility
tempStackPlot <- function(df,
yVars,
xVar="state",
yLab=NULL,
plotTitle=NULL,
colorVector=c("lightblue", "grey", "orange", "black"),
addSuffix="%",
scaleName="Cohort",
textBuffer=0.5,
makeDotPlot=FALSE,
yLims=NULL
) {
# FUNCTION ARGUMENTS:
# df: data frame or tibble
# yVars: named vector with c("variable"="name"), in the desired order from right-most to left-most
# xVar: the x variable
# yLab: the y-axis label for the plot
# plotTitle: the title for the plot
# colorVector: colors to use for filled bars (sequentially, can have more, but not less, than length(yVars))
# addSuffix: value to be appended to all values in plots (e.g., 96 would show as 96% in the text label)
# scaleName: the name to use for the legend
# textBuffer: distance from bar to text
# makeDotPlot: boolean, should a dot-plot be made rather than stacked bars?
# yLims: the limits for the y-axis passed as a length-2 vector such as c(0, 100) or c(0, NA)
colorVector <- colorVector[1:length(yVars)]
names(colorVector) <- names(yVars)
# Function for the legend
fnLegendKey <- if(isTRUE(makeDotPlot)) scale_color_manual else scale_fill_manual
p1 <- df %>%
select(all_of(xVar), all_of(names(yVars))) %>%
pivot_longer(-c(all_of(xVar))) %>%
ggplot(aes(x=fct_reorder(get(xVar[1]), value, max))) +
coord_flip() +
labs(x=NULL, y=yLab, title=plotTitle) +
(if(isTRUE(makeDotPlot)) geom_point(aes(y=value, color=name))
else geom_col(aes(y=value, fill=name), position="identity")
) +
geom_text(aes(y=value+textBuffer,
label=paste0(value,
addSuffix,
ifelse(name==names(yVars)[1], paste0(" (", get(xVar[1]), ")"), "")
)
),
hjust=0,
size=3
) +
fnLegendKey(scaleName,
breaks=rev(names(yVars)),
labels=rev(unname(yVars)),
values=colorVector
) +
theme(legend.position="bottom")
# Add the y-limits if appropriate
if (!is.null(yLims)) p1 <- p1 + lims(y=yLims)
p1
}
# Create data
cdcBurdenGrowth <- cdc_daily_210815$dfPerCapita %>%
filter(date %in% c(as.Date(max(date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for cases
p1 <- cdcBurdenGrowth %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=c("2021-08-13"="2021-08-13",
"2021-02-11"="2021-02-11",
"2020-08-12"="2020-08-12"
),
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date"
)
# Run for deaths
p2 <- cdcBurdenGrowth %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=c("2021-08-13"="2021-08-13",
"2021-02-11"="2021-02-11",
"2020-08-12"="2020-08-12"
),
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The latest data as of August 31 are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_210902.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_210902.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_210902.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210804")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210804")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210804")$dfRaw$vax
)
cdc_daily_210902 <- readRunCDCDaily(thruLabel="Sep 1, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 0 143 143 2.00000000
## 2 2020-02-03 tot_deaths 178 143 35 0.21806854
## 3 2020-02-04 tot_deaths 178 143 35 0.21806854
## 4 2020-02-05 tot_deaths 178 143 35 0.21806854
## 5 2020-02-06 tot_deaths 178 143 35 0.21806854
## 6 2020-02-07 tot_deaths 178 143 35 0.21806854
## 7 2020-02-08 tot_deaths 179 144 35 0.21671827
## 8 2020-02-09 tot_deaths 179 144 35 0.21671827
## 9 2020-02-10 tot_deaths 179 144 35 0.21671827
## 10 2020-02-11 tot_deaths 179 144 35 0.21671827
## 11 2020-02-12 tot_deaths 179 144 35 0.21671827
## 12 2020-02-13 tot_deaths 179 144 35 0.21671827
## 13 2020-02-14 tot_deaths 179 144 35 0.21671827
## 14 2020-02-15 tot_deaths 179 144 35 0.21671827
## 15 2020-02-16 tot_deaths 179 144 35 0.21671827
## 16 2020-02-17 tot_deaths 179 144 35 0.21671827
## 17 2020-02-18 tot_deaths 179 144 35 0.21671827
## 18 2020-02-19 tot_deaths 180 145 35 0.21538462
## 19 2020-02-20 tot_deaths 180 145 35 0.21538462
## 20 2020-02-21 tot_deaths 180 145 35 0.21538462
## 21 2020-02-22 tot_deaths 180 145 35 0.21538462
## 22 2020-02-23 tot_deaths 180 145 35 0.21538462
## 23 2020-02-24 tot_deaths 180 145 35 0.21538462
## 24 2020-02-25 tot_deaths 180 145 35 0.21538462
## 25 2020-02-26 tot_deaths 180 145 35 0.21538462
## 26 2020-02-27 tot_deaths 181 146 35 0.21406728
## 27 2020-02-28 tot_deaths 181 146 35 0.21406728
## 28 2020-02-29 tot_deaths 182 147 35 0.21276596
## 29 2020-03-01 tot_deaths 182 147 35 0.21276596
## 30 2020-03-02 tot_deaths 188 153 35 0.20527859
## 31 2020-03-06 tot_deaths 200 163 37 0.20385675
## 32 2020-03-05 tot_deaths 196 160 36 0.20224719
## 33 2020-03-03 tot_deaths 191 156 35 0.20172911
## 34 2020-03-04 tot_deaths 193 158 35 0.19943020
## 35 2020-03-07 tot_deaths 205 168 37 0.19839142
## 36 2020-03-08 tot_deaths 210 173 37 0.19321149
## 37 2020-03-10 tot_deaths 220 184 36 0.17821782
## 38 2020-03-09 tot_deaths 214 179 35 0.17811705
## 39 2020-03-11 tot_deaths 234 198 36 0.16666667
## 40 2020-03-12 tot_deaths 241 205 36 0.16143498
## 41 2020-03-13 tot_deaths 252 216 36 0.15384615
## 42 2020-03-14 tot_deaths 266 229 37 0.14949495
## 43 2020-03-15 tot_deaths 286 249 37 0.13831776
## 44 2020-03-16 tot_deaths 307 271 36 0.12456747
## 45 2020-03-17 tot_deaths 339 301 38 0.11875000
## 46 2020-03-18 tot_deaths 410 373 37 0.09450830
## 47 2020-03-20 tot_deaths 577 530 47 0.08491418
## 48 2020-03-19 tot_deaths 475 437 38 0.08333333
## 49 2020-03-21 tot_deaths 692 644 48 0.07185629
## 50 2020-03-22 tot_deaths 827 775 52 0.06491885
## 51 2020-03-25 tot_deaths 1544 1459 85 0.05661006
## 52 2020-03-24 tot_deaths 1232 1165 67 0.05590321
## 53 2020-03-23 tot_deaths 998 946 52 0.05349794
## 54 2020-03-26 tot_deaths 1897 1804 93 0.05025669
## 55 2020-02-02 tot_cases 56 510 454 1.60424028
## 56 2021-07-25 new_deaths 261 136 125 0.62972292
## 57 2021-07-24 new_deaths 287 162 125 0.55679287
## 58 2021-07-18 new_deaths 166 96 70 0.53435115
## 59 2021-07-23 new_deaths 388 226 162 0.52768730
## 60 2021-08-01 new_deaths 363 240 123 0.40796020
## 61 2021-07-26 new_deaths 368 245 123 0.40130506
## 62 2021-06-08 new_deaths 313 209 104 0.39846743
## 63 2021-06-07 new_deaths 339 500 161 0.38379023
## 64 2020-11-11 new_deaths 1516 1046 470 0.36690086
## 65 2021-07-31 new_deaths 397 274 123 0.36661699
## 66 2021-07-17 new_deaths 196 138 58 0.34730539
## 67 2021-07-19 new_deaths 256 185 71 0.32199546
## 68 2020-11-12 new_deaths 1366 1863 497 0.30783524
## 69 2020-11-10 new_deaths 1310 1761 451 0.29371540
## 70 2021-08-02 new_deaths 523 392 131 0.28633880
## 71 2021-07-05 new_deaths 141 106 35 0.28340081
## 72 2021-07-04 new_deaths 131 101 30 0.25862069
## 73 2021-06-17 new_deaths 268 334 66 0.21926910
## 74 2021-07-28 new_deaths 443 356 87 0.21777222
## 75 2021-07-29 new_deaths 460 370 90 0.21686747
## 76 2021-05-31 new_deaths 268 218 50 0.20576132
## 77 2020-09-30 new_deaths 537 660 123 0.20551378
## 78 2021-07-12 new_deaths 227 186 41 0.19854722
## 79 2021-06-13 new_deaths 167 200 33 0.17983651
## 80 2020-10-11 new_deaths 565 675 110 0.17741935
## 81 2021-06-21 new_deaths 265 315 50 0.17241379
## 82 2021-07-15 new_deaths 298 251 47 0.17122040
## 83 2020-07-13 new_deaths 869 732 137 0.17114304
## 84 2021-07-11 new_deaths 140 118 22 0.17054264
## 85 2021-07-10 new_deaths 153 129 24 0.17021277
## 86 2021-07-27 new_deaths 464 393 71 0.16569428
## 87 2020-10-08 new_deaths 681 803 122 0.16442049
## 88 2021-06-14 new_deaths 221 260 39 0.16216216
## 89 2021-07-20 new_deaths 316 269 47 0.16068376
## 90 2021-07-06 new_deaths 203 173 30 0.15957447
## 91 2021-06-01 new_deaths 334 391 57 0.15724138
## 92 2020-09-24 new_deaths 694 812 118 0.15670651
## 93 2020-07-26 new_deaths 937 802 135 0.15526164
## 94 2020-09-07 new_deaths 557 477 80 0.15473888
## 95 2021-07-22 new_deaths 331 386 55 0.15341702
## 96 2020-08-02 new_deaths 919 789 130 0.15222482
## 97 2020-09-23 new_deaths 812 944 132 0.15034169
## 98 2020-08-30 new_deaths 638 552 86 0.14453782
## 99 2021-04-11 new_deaths 428 371 57 0.14267835
## 100 2021-07-16 new_deaths 301 261 40 0.14234875
## 101 2020-09-10 new_deaths 789 908 119 0.14024750
## 102 2021-07-09 new_deaths 268 233 35 0.13972056
## 103 2020-07-12 new_deaths 865 753 112 0.13844252
## 104 2020-09-13 new_deaths 713 623 90 0.13473054
## 105 2021-06-19 new_deaths 206 180 26 0.13471503
## 106 2021-05-29 new_deaths 351 307 44 0.13373860
## 107 2020-10-15 new_deaths 693 792 99 0.13333333
## 108 2020-09-09 new_deaths 840 959 119 0.13229572
## 109 2020-09-06 new_deaths 661 580 81 0.13053989
## 110 2020-09-20 new_deaths 473 416 57 0.12823397
## 111 2020-08-09 new_deaths 856 756 100 0.12406948
## 112 2020-07-19 new_deaths 981 869 112 0.12108108
## 113 2021-03-26 new_deaths 844 951 107 0.11922006
## 114 2021-07-03 new_deaths 160 142 18 0.11920530
## 115 2021-07-07 new_deaths 239 269 30 0.11811024
## 116 2021-06-24 new_deaths 255 287 32 0.11808118
## 117 2020-08-17 new_deaths 834 742 92 0.11675127
## 118 2020-07-20 new_deaths 1028 915 113 0.11631498
## 119 2020-09-28 new_deaths 518 462 56 0.11428571
## 120 2020-08-04 new_deaths 1217 1087 130 0.11284722
## 121 2020-07-27 new_deaths 1115 997 118 0.11174242
## 122 2020-09-17 new_deaths 698 778 80 0.10840108
## 123 2021-06-04 new_deaths 522 469 53 0.10696266
## 124 2020-07-25 new_deaths 1153 1036 117 0.10689813
## 125 2021-06-12 new_deaths 298 268 30 0.10600707
## 126 2020-07-05 new_deaths 571 514 57 0.10506912
## 127 2020-07-06 new_deaths 716 645 71 0.10433505
## 128 2021-05-30 new_deaths 263 237 26 0.10400000
## 129 2020-09-11 new_deaths 797 884 87 0.10350982
## 130 2021-06-05 new_deaths 319 288 31 0.10214168
## 131 2020-07-09 new_deaths 883 798 85 0.10113028
## 132 2021-05-27 new_deaths 508 561 53 0.09915809
## 133 2020-09-21 new_deaths 657 596 61 0.09736632
## 134 2020-09-18 new_deaths 773 852 79 0.09723077
## 135 2020-07-04 new_deaths 572 519 53 0.09715857
## 136 2020-09-16 new_deaths 985 1085 100 0.09661836
## 137 2021-06-06 new_deaths 225 247 22 0.09322034
## 138 2020-08-06 new_deaths 1236 1126 110 0.09314141
## 139 2020-07-18 new_deaths 1001 912 89 0.09304757
## 140 2020-03-20 new_deaths 102 93 9 0.09230769
## 141 2020-10-09 new_deaths 745 816 71 0.09096733
## 142 2020-06-22 new_deaths 583 533 50 0.08960573
## 143 2021-07-01 new_deaths 216 236 20 0.08849558
## 144 2020-10-01 new_deaths 704 768 64 0.08695652
## 145 2021-06-20 new_deaths 193 177 16 0.08648649
## 146 2020-08-23 new_deaths 753 692 61 0.08442907
## 147 2021-06-30 new_deaths 247 227 20 0.08438819
## 148 2020-10-10 new_deaths 656 603 53 0.08419380
## 149 2020-09-15 new_deaths 793 862 69 0.08338369
## 150 2020-10-13 new_deaths 741 805 64 0.08279431
## 151 2021-07-02 new_deaths 265 244 21 0.08251473
## 152 2021-06-18 new_deaths 228 210 18 0.08219178
## 153 2020-08-24 new_deaths 779 718 61 0.08149633
## 154 2021-05-20 new_deaths 498 540 42 0.08092486
## 155 2021-05-25 new_deaths 491 532 41 0.08015640
## 156 2020-09-01 new_deaths 960 1040 80 0.08000000
## 157 2020-09-03 new_deaths 882 954 72 0.07843137
## 158 2021-06-15 new_deaths 311 336 25 0.07727975
## 159 2021-03-25 new_deaths 762 823 61 0.07697161
## 160 2020-09-27 new_deaths 489 453 36 0.07643312
## 161 2020-09-12 new_deaths 599 646 47 0.07550201
## 162 2020-08-31 new_deaths 688 638 50 0.07541478
## 163 2020-09-25 new_deaths 705 760 55 0.07508532
## 164 2020-06-14 new_deaths 491 456 35 0.07391763
## 165 2020-07-17 new_deaths 1097 1020 77 0.07274445
## 166 2020-08-18 new_deaths 1034 1112 78 0.07269338
## 167 2020-09-29 new_deaths 862 927 65 0.07266629
## 168 2020-07-24 new_deaths 1337 1244 93 0.07206509
## 169 2021-06-26 new_deaths 188 175 13 0.07162534
## 170 2020-10-07 new_deaths 791 849 58 0.07073171
## 171 2021-02-16 new_deaths 1579 1694 115 0.07027192
## 172 2021-06-28 new_deaths 207 193 14 0.07000000
## 173 2020-10-25 new_deaths 654 610 44 0.06962025
## 174 2021-04-16 new_deaths 755 809 54 0.06905371
## 175 2021-05-09 new_deaths 383 358 25 0.06747638
## 176 2021-03-14 new_deaths 635 594 41 0.06672091
## 177 2021-03-30 new_deaths 725 775 50 0.06666667
## 178 2021-04-05 new_deaths 404 378 26 0.06649616
## 179 2020-03-24 new_deaths 234 219 15 0.06622517
## 180 2020-07-15 new_deaths 1171 1096 75 0.06616674
## 181 2021-06-16 new_deaths 331 310 21 0.06552262
## 182 2020-09-14 new_deaths 538 504 34 0.06525912
## 183 2021-05-18 new_deaths 615 656 41 0.06451613
## 184 2020-07-21 new_deaths 1332 1251 81 0.06271777
## 185 2021-05-04 new_deaths 698 743 45 0.06245663
## 186 2021-05-21 new_deaths 590 628 38 0.06239737
## 187 2020-08-14 new_deaths 1011 1076 65 0.06229037
## 188 2020-08-25 new_deaths 975 1037 62 0.06163022
## 189 2020-12-25 new_deaths 2489 2341 148 0.06128364
## 190 2021-07-30 new_deaths 488 459 29 0.06124604
## 191 2020-10-21 new_deaths 1046 1112 66 0.06116775
## 192 2020-07-11 new_deaths 880 828 52 0.06088993
## 193 2021-04-26 new_deaths 496 527 31 0.06060606
## 194 2020-08-01 new_deaths 1144 1077 67 0.06033318
## 195 2021-06-02 new_deaths 472 501 29 0.05960946
## 196 2020-03-25 new_deaths 312 294 18 0.05940594
## 197 2021-05-26 new_deaths 497 527 30 0.05859375
## 198 2020-06-21 new_deaths 406 383 23 0.05830165
## 199 2021-01-01 new_deaths 3181 3001 180 0.05823358
## 200 2021-04-25 new_deaths 425 401 24 0.05811138
## 201 2020-08-22 new_deaths 898 848 50 0.05727377
## 202 2020-10-17 new_deaths 666 705 39 0.05689278
## 203 2021-06-27 new_deaths 147 139 8 0.05594406
## 204 2020-08-28 new_deaths 950 899 51 0.05516495
## 205 2021-04-06 new_deaths 726 767 41 0.05492297
## 206 2020-08-20 new_deaths 974 922 52 0.05485232
## 207 2020-10-02 new_deaths 772 815 43 0.05419030
## 208 2021-05-02 new_deaths 408 387 21 0.05283019
## 209 2021-04-08 new_deaths 741 781 40 0.05256242
## 210 2020-07-22 new_deaths 1204 1143 61 0.05198125
## 211 2020-06-29 new_deaths 537 510 27 0.05157593
## 212 2021-03-16 new_deaths 889 936 47 0.05150685
## 213 2021-02-19 new_deaths 2138 2251 113 0.05149237
## 214 2021-05-16 new_deaths 419 398 21 0.05140759
## 215 2021-06-10 new_deaths 381 401 20 0.05115090
## 216 2021-04-19 new_deaths 503 478 25 0.05096840
## 217 2020-06-27 new_deaths 585 556 29 0.05083260
## 218 2020-05-04 new_deaths 1341 1275 66 0.05045872
## 219 2021-07-21 new_deaths 387 407 20 0.05037783
## 220 2021-03-09 new_deaths 1067 1122 55 0.05025126
## 221 2020-11-07 new_cases 131351 95671 35680 0.31433077
## 222 2021-07-29 new_cases 100196 83462 16734 0.18223001
## 223 2020-08-31 new_cases 36964 31737 5227 0.15216664
## 224 2021-07-26 new_cases 58003 51051 6952 0.12749647
## 225 2021-07-28 new_cases 95770 84435 11335 0.12580117
## 226 2020-09-01 new_cases 39422 44538 5116 0.12186756
## 227 2021-07-27 new_cases 87274 77663 9611 0.11654147
## 228 2021-03-01 new_cases 51474 45900 5574 0.11448641
## 229 2021-06-06 new_cases 10806 12103 1297 0.11323061
## 230 2021-07-06 new_cases 16808 15036 1772 0.11129255
## 231 2021-06-28 new_cases 10809 9695 1114 0.10866172
## 232 2021-04-12 new_cases 60933 54867 6066 0.10476684
## 233 2021-01-02 new_cases 202831 223976 21145 0.09908460
## 234 2021-07-20 new_cases 53097 48234 4863 0.09598247
## 235 2020-07-05 new_cases 41454 45432 3978 0.09156826
## 236 2020-10-11 new_cases 43907 47898 3991 0.08694516
## 237 2020-12-26 new_cases 139718 151877 12159 0.08339649
## 238 2021-07-31 new_cases 93001 85965 7036 0.07862946
## 239 2021-04-18 new_cases 47829 51724 3895 0.07824978
## 240 2020-06-25 new_cases 52896 48970 3926 0.07708166
## 241 2020-10-10 new_cases 54532 50606 3926 0.07468280
## 242 2021-05-22 new_cases 21567 23239 1672 0.07463286
## 243 2020-05-28 new_cases 25872 24023 1849 0.07411564
## 244 2021-04-11 new_cases 49741 53464 3723 0.07214767
## 245 2021-07-12 new_cases 24378 22694 1684 0.07154997
## 246 2020-11-26 new_cases 154983 166180 11197 0.06972783
## 247 2020-07-11 new_cases 71048 66320 4728 0.06883699
## 248 2020-06-28 new_cases 42359 45369 3010 0.06862119
## 249 2020-07-13 new_cases 54767 58407 3640 0.06432573
## 250 2021-07-19 new_cases 37518 35205 2313 0.06361124
## 251 2021-02-28 new_cases 48809 51996 3187 0.06323099
## 252 2020-07-15 new_cases 74721 70325 4396 0.06061525
## 253 2021-06-05 new_cases 14629 15543 914 0.06058597
## 254 2020-10-04 new_cases 34481 36560 2079 0.05852958
## 255 2020-07-07 new_cases 61055 57638 3417 0.05757711
## 256 2020-08-09 new_cases 41301 43702 2401 0.05649212
## 257 2020-05-21 new_cases 26310 27823 1513 0.05589936
## 258 2021-06-25 new_cases 14346 13571 775 0.05552173
## 259 2021-07-24 new_cases 61423 58168 3255 0.05443553
## 260 2021-02-15 new_cases 55391 52487 2904 0.05383860
## 261 2021-04-24 new_cases 52189 55068 2879 0.05368414
## 262 2020-08-15 new_cases 44136 46541 2405 0.05304542
## 263 2020-07-19 new_cases 58071 61214 3143 0.05269732
## 264 2020-10-21 new_cases 73143 69480 3663 0.05136619
## 265 2020-07-01 new_cases 62056 58987 3069 0.05070925
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FL tot_deaths 10350041 9728382 621659 0.061923090
## 2 PR tot_deaths 628469 609082 19387 0.031331234
## 3 KY tot_deaths 1849448 1839137 10311 0.005590762
## 4 IN tot_deaths 3811948 3798160 13788 0.003623602
## 5 MS tot_deaths 2215491 2207767 7724 0.003492448
## 6 AL tot_deaths 3073211 3066125 7086 0.002308393
## 7 SC tot_deaths 2563582 2568618 5036 0.001962511
## 8 NM tot_deaths 1126633 1124549 2084 0.001851472
## 9 NC tot_deaths 3458165 3454115 4050 0.001171828
## 10 PR tot_cases 31617012 33334437 1717425 0.052883347
## 11 PA tot_cases 273389806 270659767 2730039 0.010035994
## 12 AL tot_cases 147055558 148145611 1090053 0.007385154
## 13 SC tot_cases 145835188 146346674 511486 0.003501148
## 14 FL tot_cases 578444524 576560229 1884295 0.003262835
## 15 MI tot_cases 242865368 242316039 549329 0.002264427
## 16 FL new_deaths 40473 39179 1294 0.032491337
## 17 MS new_deaths 7791 7544 247 0.032213890
## 18 GA new_deaths 21232 21698 466 0.021709760
## 19 KY new_deaths 7479 7348 131 0.017670466
## 20 AL new_deaths 11734 11542 192 0.016497680
## 21 NM new_deaths 4446 4414 32 0.007223476
## 22 NC new_deaths 13739 13670 69 0.005034843
## 23 IN new_deaths 14058 14012 46 0.003277520
## 24 TN new_deaths 12794 12758 36 0.002817783
## 25 PR new_deaths 2591 2585 6 0.002318393
## 26 SC new_deaths 9972 9958 14 0.001404917
## 27 AL new_cases 599948 592417 7531 0.012632038
## 28 CA new_cases 4074510 4037808 36702 0.009048462
## 29 NC new_cases 1064603 1056699 7904 0.007452027
## 30 WA new_cases 479286 475881 3405 0.007129643
## 31 FL new_cases 2659034 2641696 17338 0.006541740
## 32 KY new_cases 488696 486115 2581 0.005295385
## 33 GA new_cases 1190936 1185594 5342 0.004495630
## 34 MI new_cases 1017675 1013112 4563 0.004493824
## 35 PA new_cases 1232900 1227519 5381 0.004374052
## 36 TN new_cases 903587 900418 3169 0.003513294
## 37 SD new_cases 125481 125216 265 0.002114106
## 38 PR new_cases 148067 147820 247 0.001669556
## 39 SC new_cases 624678 623861 817 0.001308730
##
##
##
## Raw file for cdcDaily:
## Rows: 35,280
## Columns: 15
## $ date <date> 2021-01-25, 2020-04-17, 2021-02-02, 2020-07-30, 2020-0~
## $ state <chr> "NE", "VI", "IL", "ME", "WI", "ND", "GU", "CT", "WI", "~
## $ tot_cases <dbl> 187923, 54, 1130917, 3910, 25480, 6602, 0, 267337, 9844~
## $ conf_cases <dbl> NA, NA, 1130917, 3497, 22932, 6602, NA, 250915, 92712, ~
## $ prob_cases <dbl> NA, NA, 0, 413, 2548, 0, NA, 16422, 5728, NA, 123700, N~
## $ new_cases <dbl> 646, 1, 2304, 22, 185, 133, 0, 0, 1502, 128, 502, 0, 39~
## $ pnew_case <dbl> 0, NA, 0, 2, 11, 0, NA, 0, 94, 0, 143, NA, 5, 154, 0, 0~
## $ tot_deaths <dbl> 1894, 2, 21336, 123, 700, 103, 0, 7381, 1237, 5586, 111~
## $ conf_death <dbl> NA, NA, 19306, 122, 694, NA, NA, 6049, 1228, NA, 8887, ~
## $ prob_death <dbl> NA, NA, 2030, 1, 6, NA, NA, 1332, 9, NA, 2265, NA, NA, ~
## $ new_deaths <dbl> 15, 1, 63, 2, 2, 0, 0, 0, 8, 0, 6, 0, 32, 30, 15, 18, 2~
## $ pnew_death <dbl> 0, NA, 16, 0, 0, 0, NA, 0, 0, 0, 1, NA, 0, 4, 0, 0, 0, ~
## $ created_at <chr> "01/27/2021 12:00:00 AM", "04/17/2020 04:22:39 PM", "02~
## $ consent_cases <chr> "Not agree", NA, "Agree", "Agree", "Agree", "Agree", "N~
## $ consent_deaths <chr> "Not agree", NA, "Agree", "Agree", "Agree", "Not agree"~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 32
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 inp 18509 17110 1399 0.07855358
## 2 2021-07-31 inp 51488 48807 2681 0.05346229
## 3 2020-07-25 hosp_ped 3450 4610 1160 0.28784119
## 4 2020-08-02 hosp_ped 4092 4498 406 0.09452852
## 5 2021-07-31 hosp_ped 1313 1247 66 0.05156250
## 6 2021-07-02 hosp_ped 696 662 34 0.05007364
## 7 2021-07-02 hosp_adult 17813 16448 1365 0.07968244
## 8 2021-07-31 hosp_adult 50175 47560 2615 0.05351205
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL inp 544475 542977 1498 0.002755064
## 2 TX inp 2869774 2864342 5432 0.001894625
## 3 NC inp 675079 673836 1243 0.001842963
## 4 PR inp 153817 153545 272 0.001769900
## 5 NH hosp_ped 330 285 45 0.146341463
## 6 ME hosp_ped 467 516 49 0.099694812
## 7 VI hosp_ped 31 33 2 0.062500000
## 8 KY hosp_ped 5435 5760 325 0.058061635
## 9 KS hosp_ped 1718 1819 101 0.057110546
## 10 DE hosp_ped 1826 1754 72 0.040223464
## 11 NV hosp_ped 2279 2190 89 0.039829940
## 12 AR hosp_ped 6174 6372 198 0.031563845
## 13 WV hosp_ped 2221 2289 68 0.030155211
## 14 IN hosp_ped 7226 7418 192 0.026222344
## 15 NM hosp_ped 3202 3279 77 0.023761765
## 16 ID hosp_ped 1422 1389 33 0.023479189
## 17 MA hosp_ped 5244 5129 115 0.022172949
## 18 AZ hosp_ped 11698 11884 186 0.015774743
## 19 SC hosp_ped 2805 2843 38 0.013456091
## 20 VA hosp_ped 6956 6870 86 0.012440330
## 21 MO hosp_ped 16702 16901 199 0.011844181
## 22 IA hosp_ped 2469 2441 28 0.011405295
## 23 TN hosp_ped 8139 8223 84 0.010267693
## 24 UT hosp_ped 3369 3337 32 0.009543692
## 25 MS hosp_ped 4255 4295 40 0.009356725
## 26 AL hosp_ped 8314 8385 71 0.008503503
## 27 CO hosp_ped 9706 9784 78 0.008004105
## 28 MD hosp_ped 5209 5241 32 0.006124402
## 29 SD hosp_ped 2503 2489 14 0.005608974
## 30 PR hosp_ped 11806 11868 62 0.005237814
## 31 LA hosp_ped 3659 3676 17 0.004635310
## 32 FL hosp_ped 56499 56703 204 0.003604177
## 33 OH hosp_ped 28273 28343 70 0.002472799
## 34 TX hosp_ped 41536 41439 97 0.002338054
## 35 OK hosp_ped 12409 12436 27 0.002173476
## 36 CT hosp_ped 2467 2462 5 0.002028809
## 37 OR hosp_ped 3144 3150 6 0.001906578
## 38 WA hosp_ped 4613 4621 8 0.001732727
## 39 CA hosp_ped 32309 32259 50 0.001548755
## 40 IL hosp_ped 20519 20548 29 0.001412326
## 41 RI hosp_ped 1454 1452 2 0.001376462
## 42 GA hosp_ped 22950 22921 29 0.001264415
## 43 NJ hosp_ped 9322 9311 11 0.001180701
## 44 AL hosp_adult 463714 462110 1604 0.003465021
## 45 PR hosp_adult 120801 120467 334 0.002768705
## 46 TX hosp_adult 2415904 2411369 4535 0.001878908
## 47 VI hosp_adult 1703 1700 3 0.001763150
## 48 KY hosp_adult 310588 310043 545 0.001756277
## 49 ME hosp_adult 38523 38476 47 0.001220795
##
##
##
## Raw file for cdcHosp:
## Rows: 29,378
## Columns: 99
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_210902.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 16,998
## Columns: 69
## $ date <date> 2021-09-01, 2021-09-01, 2021-0~
## $ MMWR_week <dbl> 35, 35, 35, 35, 35, 35, 35, 35,~
## $ state <chr> "KS", "NY", "RP", "NC", "IN", "~
## $ Distributed <dbl> 3585815, 27518855, 29820, 13316~
## $ Distributed_Janssen <dbl> 173500, 1253900, 3800, 646500, ~
## $ Distributed_Moderna <dbl> 1498900, 10968420, 20800, 54326~
## $ Distributed_Pfizer <dbl> 1913415, 15296535, 5220, 723687~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 123084, 141459, 166527, 126963,~
## $ Distributed_Per_100k_12Plus <dbl> 146258, 164064, 195004, 148194,~
## $ Distributed_Per_100k_18Plus <dbl> 162029, 178401, 213809, 162641,~
## $ Distributed_Per_100k_65Plus <dbl> 754135, 834880, 982861, 760441,~
## $ vxa <dbl> 2919234, 24428883, 29007, 10495~
## $ Administered_12Plus <dbl> 2919103, 24426700, 29007, 10481~
## $ Administered_18Plus <dbl> 2734777, 22985019, 26820, 98589~
## $ Administered_65Plus <dbl> 825173, 5633411, 3180, 2918484,~
## $ Administered_Janssen <dbl> 101837, 1072305, 2256, 390218, ~
## $ Administered_Moderna <dbl> 1185497, 9356997, 23828, 403072~
## $ Administered_Pfizer <dbl> 1630976, 13991672, 2923, 607437~
## $ Administered_Unk_Manuf <dbl> 924, 7909, 0, 463, 23170, 19, 8~
## $ Administered_Fed_LTC <dbl> 90122, 441057, 0, 228485, 14339~
## $ Administered_Fed_LTC_Residents <dbl> 45956, 211789, 0, 112435, 91660~
## $ Administered_Fed_LTC_Staff <dbl> 28092, 156003, 0, 73210, 37398,~
## $ Administered_Fed_LTC_Unk <dbl> 16074, 73265, 0, 42840, 14337, ~
## $ Administered_Fed_LTC_Dose1 <dbl> 50821, 263888, 0, 137030, 81403~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 24675, 119940, 0, 63622, 50214,~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 16838, 90356, 0, 42876, 21594, ~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 9308, 53592, 0, 30532, 9595, 32~
## $ Admin_Per_100k <dbl> 100203, 125575, 161987, 100073,~
## $ Admin_Per_100k_12Plus <dbl> 119065, 145629, 189687, 116651,~
## $ Admin_Per_100k_18Plus <dbl> 123574, 149009, 192299, 120417,~
## $ Admin_Per_100k_65Plus <dbl> 173543, 170909, 104812, 166666,~
## $ Recip_Administered <dbl> 3015591, 24362987, 29253, 10393~
## $ Administered_Dose1_Recip <dbl> 1673114, 13136849, 16802, 58541~
## $ Administered_Dose1_Pop_Pct <dbl> 57.4, 67.5, 93.8, 55.8, 50.2, 5~
## $ Administered_Dose1_Recip_12Plus <dbl> 1672986, 13135259, 16802, 58453~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 68.2, 78.3, 99.9, 65.1, 59.3, 6~
## $ Administered_Dose1_Recip_18Plus <dbl> 1563564, 12335911, 15524, 54900~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 70.7, 80.0, 99.9, 67.1, 61.7, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 456535, 2951707, 1761, 1528477,~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 96.0, 89.6, 58.0, 87.3, 85.4, 8~
## $ vxc <dbl> 1408917, 11725228, 14704, 48718~
## $ vxcpoppct <dbl> 48.4, 60.3, 82.1, 46.5, 46.4, 4~
## $ Series_Complete_12Plus <dbl> 1408894, 11724573, 14704, 48665~
## $ Series_Complete_12PlusPop_Pct <dbl> 57.5, 69.9, 96.2, 54.2, 54.7, 5~
## $ vxcgte18 <dbl> 1328055, 11080352, 13791, 46053~
## $ vxcgte18pct <dbl> 60.0, 71.8, 98.9, 56.2, 57.3, 5~
## $ vxcgte65 <dbl> 394170, 2719740, 1687, 1373717,~
## $ vxcgte65pct <dbl> 82.9, 82.5, 55.6, 78.4, 82.8, 7~
## $ Series_Complete_Janssen <dbl> 100534, 1019101, 2260, 383611, ~
## $ Series_Complete_Moderna <dbl> 550051, 4326607, 11443, 1789647~
## $ Series_Complete_Pfizer <dbl> 757989, 6377516, 1001, 2698491,~
## $ Series_Complete_Unk_Manuf <dbl> 343, 2004, 0, 80, 7008, 31, 757~
## $ Series_Complete_Janssen_12Plus <dbl> 100527, 1018977, 2260, 383589, ~
## $ Series_Complete_Moderna_12Plus <dbl> 550044, 4326446, 11443, 1789554~
## $ Series_Complete_Pfizer_12Plus <dbl> 757980, 6377160, 1001, 2693362,~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 343, 1990, 0, 80, 7008, 31, 757~
## $ Series_Complete_Janssen_18Plus <dbl> 100443, 1018434, 2260, 382326, ~
## $ Series_Complete_Moderna_18Plus <dbl> 549764, 4324978, 11443, 1786935~
## $ Series_Complete_Pfizer_18Plus <dbl> 677527, 5735029, 88, 2436020, 1~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 321, 1911, 0, 78, 6947, 30, 698~
## $ Series_Complete_Janssen_65Plus <dbl> 16575, 168165, 221, 44400, 2774~
## $ Series_Complete_Moderna_65Plus <dbl> 189386, 1295280, 1456, 660640, ~
## $ Series_Complete_Pfizer_65Plus <dbl> 188040, 1255416, 10, 668639, 42~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 169, 879, 0, 38, 3493, 18, 160,~
## $ Series_Complete_FedLTC <dbl> 39003, 175255, 0, 90598, 61904,~
## $ Series_Complete_FedLTC_Residents <dbl> 20959, 90116, 0, 47822, 41207, ~
## $ Series_Complete_FedLTC_Staff <dbl> 11153, 64362, 0, 29613, 15716, ~
## $ Series_Complete_FedLTC_Unknown <dbl> 6891, 20777, 0, 13163, 4981, 10~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 9.39e+9 1.85e+8 3.92e+7 631618 34692
## 2 after 9.35e+9 1.84e+8 3.90e+7 628537 29988
## 3 pctchg 4.25e-3 4.13e-3 4.79e-3 0.00488 0.136
##
##
## Processed for cdcDaily:
## Rows: 29,988
## Columns: 6
## $ date <date> 2021-01-25, 2021-02-02, 2020-07-30, 2020-06-15, 2020-07-31~
## $ state <chr> "NE", "IL", "ME", "WI", "ND", "CT", "WI", "NV", "AL", "MI",~
## $ tot_cases <dbl> 187923, 1130917, 3910, 25480, 6602, 267337, 98440, 324132, ~
## $ tot_deaths <dbl> 1894, 21336, 123, 700, 103, 7381, 1237, 5586, 11152, 0, 116~
## $ new_cases <dbl> 646, 2304, 22, 185, 133, 0, 1502, 128, 502, 0, 394, 603, 33~
## $ new_deaths <dbl> 15, 63, 2, 2, 0, 0, 8, 0, 6, 0, 32, 30, 15, 18, 2, 0, 39, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.06e+7 2.46e+7 531377 29378
## 2 after 3.04e+7 2.45e+7 518452 28311
## 3 pctchg 5.55e-3 5.50e-3 0.0243 0.0363
##
##
## Processed for cdcHosp:
## Rows: 28,311
## Columns: 5
## $ date <date> 2020-07-24, 2020-07-23, 2020-07-22, 2020-07-22, 2020-07-21~
## $ state <chr> "ND", "ND", "IA", "ND", "ND", "ND", "KY", "ND", "HI", "LA",~
## $ inp <dbl> 52, 54, 0, 51, 46, 44, 532, 46, 44, 1521, 42, 205, 50, 841,~
## $ hosp_adult <dbl> NA, NA, 0, NA, NA, NA, 500, NA, 27, 1482, NA, NA, NA, NA, N~
## $ hosp_ped <dbl> NA, NA, 0, NA, NA, NA, 0, NA, 0, 0, NA, NA, NA, NA, NA, NA,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.05e+11 4.47e+10 399452. 1.39e+10 708373. 4.32e+10 490082.
## 2 after 5.02e+10 2.16e+10 337472. 6.73e+ 9 644868. 2.09e+10 419494.
## 3 pctchg 5.24e- 1 5.16e- 1 0.155 5.16e- 1 0.0896 5.16e- 1 0.144
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 13,362
## Columns: 9
## $ date <date> 2021-09-01, 2021-09-01, 2021-09-01, 2021-09-01, 2021-09-0~
## $ state <chr> "KS", "NY", "NC", "IN", "NV", "TX", "NH", "MD", "FL", "AL"~
## $ vxa <dbl> 2919234, 24428883, 10495776, 6431733, 3213855, 30268539, 1~
## $ vxc <dbl> 1408917, 11725228, 4871829, 3121678, 1477405, 13802230, 81~
## $ vxcpoppct <dbl> 48.4, 60.3, 46.5, 46.4, 48.0, 47.6, 59.7, 61.6, 53.2, 38.4~
## $ vxcgte65 <dbl> 394170, 2719740, 1373717, 898666, 377734, 2913651, 222210,~
## $ vxcgte65pct <dbl> 82.9, 82.5, 78.4, 82.8, 76.2, 78.0, 87.5, 89.3, 82.3, 72.6~
## $ vxcgte18 <dbl> 1328055, 11080352, 4605359, 2961264, 1400878, 12729288, 76~
## $ vxcgte18pct <dbl> 60.0, 71.8, 56.2, 57.3, 58.7, 58.9, 69.6, 73.7, 63.2, 47.4~
##
## Integrated per capita data file:
## Rows: 30,201
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_210902)The pivoted file and summaries are also created:
hospAge_210902 <- cdc_daily_210902$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_210902## # A tibble: 528,804 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 0-19 West Nor~
## 2 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 20-29 West Nor~
## 3 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 30-39 West Nor~
## 4 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 40-49 West Nor~
## 5 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 50-59 West Nor~
## 6 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 60-69 West Nor~
## 7 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 70-79 West Nor~
## 8 ND 2020-07-24 previous_day_admiss~ NA confirm~ adult 80+ West Nor~
## 9 ND 2020-07-24 previous_day_admiss~ NA suspect~ adult 0-19 West Nor~
## 10 ND 2020-07-24 previous_day_admiss~ NA suspect~ adult 20-29 West Nor~
## # ... with 528,794 more rowsdfPivot_210902 <- makeCaseHospDeath(dfHosp=hospAge_210902, dfCaseDeath=cdc_daily_210902$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_210902## # A tibble: 329,742 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 329,732 more rows# Plot for overall trends by age group
p1 <- hospAge_210902 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Aug 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
p1 + geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
p1 + geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_210902 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Aug 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
onePageCFRPlot(dfPivot_210902, keyState="FL", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="LA", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="OR", minDate="2020-08-01")
onePageCFRPlot(dfPivot_210902, keyState="HI", minDate="2020-08-01")
The latest data are downloaded and processed, with caching to avoid multiple file downloads:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211006.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211006.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211006.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_210804")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_210804")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_210804")$dfRaw$vax
)
cdc_daily_211006 <- readRunCDCDaily(thruLabel="Oct 5, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_dc_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 63
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-02 tot_deaths 0 143 143 2.00000000
## 2 2020-02-03 tot_deaths 244 143 101 0.52196382
## 3 2020-02-04 tot_deaths 244 143 101 0.52196382
## 4 2020-02-05 tot_deaths 244 143 101 0.52196382
## 5 2020-02-06 tot_deaths 244 143 101 0.52196382
## 6 2020-02-07 tot_deaths 244 143 101 0.52196382
## 7 2020-02-08 tot_deaths 245 144 101 0.51928021
## 8 2020-02-09 tot_deaths 245 144 101 0.51928021
## 9 2020-02-10 tot_deaths 245 144 101 0.51928021
## 10 2020-02-11 tot_deaths 245 144 101 0.51928021
## 11 2020-02-12 tot_deaths 245 144 101 0.51928021
## 12 2020-02-13 tot_deaths 245 144 101 0.51928021
## 13 2020-02-14 tot_deaths 245 144 101 0.51928021
## 14 2020-02-15 tot_deaths 245 144 101 0.51928021
## 15 2020-02-16 tot_deaths 245 144 101 0.51928021
## 16 2020-02-17 tot_deaths 245 144 101 0.51928021
## 17 2020-02-18 tot_deaths 245 144 101 0.51928021
## 18 2020-02-19 tot_deaths 246 145 101 0.51662404
## 19 2020-02-20 tot_deaths 246 145 101 0.51662404
## 20 2020-02-21 tot_deaths 246 145 101 0.51662404
## 21 2020-02-22 tot_deaths 246 145 101 0.51662404
## 22 2020-02-23 tot_deaths 246 145 101 0.51662404
## 23 2020-02-24 tot_deaths 246 145 101 0.51662404
## 24 2020-02-25 tot_deaths 246 145 101 0.51662404
## 25 2020-02-26 tot_deaths 246 145 101 0.51662404
## 26 2020-02-27 tot_deaths 247 146 101 0.51399491
## 27 2020-02-28 tot_deaths 247 146 101 0.51399491
## 28 2020-02-29 tot_deaths 248 147 101 0.51139241
## 29 2020-03-01 tot_deaths 248 147 101 0.51139241
## 30 2020-03-02 tot_deaths 254 153 101 0.49631450
## 31 2020-03-03 tot_deaths 257 156 101 0.48910412
## 32 2020-03-04 tot_deaths 259 158 101 0.48441247
## 33 2020-03-05 tot_deaths 262 160 102 0.48341232
## 34 2020-03-06 tot_deaths 266 163 103 0.48018648
## 35 2020-03-07 tot_deaths 271 168 103 0.46924829
## 36 2020-03-08 tot_deaths 276 173 103 0.45879733
## 37 2020-03-09 tot_deaths 280 179 101 0.44008715
## 38 2020-03-10 tot_deaths 286 184 102 0.43404255
## 39 2020-03-11 tot_deaths 300 198 102 0.40963855
## 40 2020-03-12 tot_deaths 307 205 102 0.39843750
## 41 2020-03-13 tot_deaths 318 216 102 0.38202247
## 42 2020-03-14 tot_deaths 332 229 103 0.36720143
## 43 2020-03-15 tot_deaths 352 249 103 0.34276206
## 44 2020-03-16 tot_deaths 373 271 102 0.31677019
## 45 2020-03-17 tot_deaths 405 301 104 0.29461756
## 46 2020-03-18 tot_deaths 476 373 103 0.24263840
## 47 2020-03-19 tot_deaths 541 437 104 0.21267894
## 48 2020-03-20 tot_deaths 643 530 113 0.19266837
## 49 2020-03-21 tot_deaths 758 644 114 0.16262482
## 50 2020-03-22 tot_deaths 893 775 118 0.14148681
## 51 2020-03-23 tot_deaths 1064 946 118 0.11741294
## 52 2020-03-24 tot_deaths 1298 1165 133 0.10799838
## 53 2020-03-25 tot_deaths 1610 1459 151 0.09840339
## 54 2020-03-26 tot_deaths 1963 1804 159 0.08441731
## 55 2020-03-27 tot_deaths 2467 2311 156 0.06529929
## 56 2020-03-28 tot_deaths 3011 2840 171 0.05845155
## 57 2020-03-29 tot_deaths 3592 3403 189 0.05403860
## 58 2020-02-02 tot_cases 20 510 490 1.84905660
## 59 2020-01-22 tot_cases 7 33 26 1.30000000
## 60 2020-01-23 tot_cases 8 35 27 1.25581395
## 61 2020-01-25 tot_cases 9 39 30 1.25000000
## 62 2020-01-24 tot_cases 9 36 27 1.20000000
## 63 2020-01-30 tot_cases 13 47 34 1.13333333
## 64 2020-01-27 tot_cases 12 43 31 1.12727273
## 65 2020-01-26 tot_cases 12 42 30 1.11111111
## 66 2020-01-29 tot_cases 13 45 32 1.10344828
## 67 2020-01-28 tot_cases 13 44 31 1.08771930
## 68 2020-01-31 tot_cases 16 51 35 1.04477612
## 69 2020-02-01 tot_cases 20 55 35 0.93333333
## 70 2020-02-14 tot_cases 617 649 32 0.05055292
## 71 2020-02-15 tot_cases 622 654 32 0.05015674
## 72 2021-07-25 new_deaths 282 136 146 0.69856459
## 73 2021-07-24 new_deaths 305 162 143 0.61241970
## 74 2021-07-18 new_deaths 170 96 74 0.55639098
## 75 2021-07-23 new_deaths 388 226 162 0.52768730
## 76 2021-08-01 new_deaths 386 240 146 0.46645367
## 77 2021-07-26 new_deaths 387 245 142 0.44936709
## 78 2021-07-31 new_deaths 422 274 148 0.42528736
## 79 2021-06-08 new_deaths 315 209 106 0.40458015
## 80 2021-06-07 new_deaths 338 500 162 0.38663484
## 81 2020-11-11 new_deaths 1516 1046 470 0.36690086
## 82 2021-07-17 new_deaths 198 138 60 0.35714286
## 83 2021-08-02 new_deaths 561 392 169 0.35466946
## 84 2021-07-19 new_deaths 264 185 79 0.35189310
## 85 2020-11-12 new_deaths 1367 1863 496 0.30712074
## 86 2020-11-10 new_deaths 1310 1761 451 0.29371540
## 87 2021-07-04 new_deaths 135 101 34 0.28813559
## 88 2021-07-05 new_deaths 141 106 35 0.28340081
## 89 2021-07-27 new_deaths 511 393 118 0.26106195
## 90 2021-07-29 new_deaths 481 370 111 0.26086957
## 91 2021-07-28 new_deaths 459 356 103 0.25276074
## 92 2021-06-17 new_deaths 268 334 66 0.21926910
## 93 2021-05-31 new_deaths 270 218 52 0.21311475
## 94 2021-07-12 new_deaths 229 186 43 0.20722892
## 95 2020-09-30 new_deaths 537 660 123 0.20551378
## 96 2021-07-11 new_deaths 145 118 27 0.20532319
## 97 2021-06-13 new_deaths 165 200 35 0.19178082
## 98 2021-07-15 new_deaths 304 251 53 0.19099099
## 99 2020-10-11 new_deaths 565 675 110 0.17741935
## 100 2021-07-10 new_deaths 154 129 25 0.17667845
## 101 2021-06-21 new_deaths 264 315 51 0.17616580
## 102 2021-07-06 new_deaths 206 173 33 0.17414248
## 103 2021-07-20 new_deaths 320 269 51 0.17317487
## 104 2020-07-13 new_deaths 867 732 135 0.16885553
## 105 2020-10-08 new_deaths 681 803 122 0.16442049
## 106 2021-06-14 new_deaths 221 260 39 0.16216216
## 107 2020-09-24 new_deaths 694 812 118 0.15670651
## 108 2020-07-26 new_deaths 938 802 136 0.15632184
## 109 2020-09-07 new_deaths 557 477 80 0.15473888
## 110 2020-08-02 new_deaths 920 789 131 0.15330603
## 111 2021-06-01 new_deaths 336 391 55 0.15130674
## 112 2021-07-09 new_deaths 271 233 38 0.15079365
## 113 2020-09-23 new_deaths 812 944 132 0.15034169
## 114 2021-06-19 new_deaths 209 180 29 0.14910026
## 115 2021-07-16 new_deaths 303 261 42 0.14893617
## 116 2021-04-11 new_deaths 430 371 59 0.14731586
## 117 2020-08-30 new_deaths 638 552 86 0.14453782
## 118 2021-07-22 new_deaths 334 386 52 0.14444444
## 119 2020-09-10 new_deaths 789 908 119 0.14024750
## 120 2020-07-12 new_deaths 865 753 112 0.13844252
## 121 2020-09-13 new_deaths 713 623 90 0.13473054
## 122 2021-05-29 new_deaths 351 307 44 0.13373860
## 123 2020-10-15 new_deaths 693 792 99 0.13333333
## 124 2021-07-03 new_deaths 162 142 20 0.13157895
## 125 2020-09-09 new_deaths 841 959 118 0.13111111
## 126 2020-09-06 new_deaths 661 580 81 0.13053989
## 127 2020-09-20 new_deaths 473 416 57 0.12823397
## 128 2020-08-09 new_deaths 857 756 101 0.12523249
## 129 2020-07-19 new_deaths 983 869 114 0.12311015
## 130 2021-03-26 new_deaths 845 951 106 0.11804009
## 131 2020-07-20 new_deaths 1029 915 114 0.11728395
## 132 2020-08-17 new_deaths 834 742 92 0.11675127
## 133 2020-09-28 new_deaths 518 462 56 0.11428571
## 134 2021-07-07 new_deaths 240 269 29 0.11394892
## 135 2020-08-04 new_deaths 1217 1087 130 0.11284722
## 136 2021-05-30 new_deaths 265 237 28 0.11155378
## 137 2021-07-30 new_deaths 513 459 54 0.11111111
## 138 2020-07-27 new_deaths 1114 997 117 0.11084794
## 139 2021-06-24 new_deaths 257 287 30 0.11029412
## 140 2021-07-02 new_deaths 272 244 28 0.10852713
## 141 2020-09-17 new_deaths 698 778 80 0.10840108
## 142 2021-06-04 new_deaths 522 469 53 0.10696266
## 143 2020-07-25 new_deaths 1153 1036 117 0.10689813
## 144 2021-06-12 new_deaths 298 268 30 0.10600707
## 145 2021-06-05 new_deaths 320 288 32 0.10526316
## 146 2020-07-05 new_deaths 571 514 57 0.10506912
## 147 2020-07-06 new_deaths 716 645 71 0.10433505
## 148 2020-09-11 new_deaths 797 884 87 0.10350982
## 149 2020-07-09 new_deaths 883 798 85 0.10113028
## 150 2020-09-21 new_deaths 657 596 61 0.09736632
## 151 2020-09-18 new_deaths 773 852 79 0.09723077
## 152 2020-07-04 new_deaths 572 519 53 0.09715857
## 153 2021-06-20 new_deaths 195 177 18 0.09677419
## 154 2020-09-16 new_deaths 985 1085 100 0.09661836
## 155 2021-05-27 new_deaths 510 561 51 0.09523810
## 156 2020-08-06 new_deaths 1236 1126 110 0.09314141
## 157 2020-07-18 new_deaths 1001 912 89 0.09304757
## 158 2021-06-26 new_deaths 192 175 17 0.09264305
## 159 2020-03-20 new_deaths 102 93 9 0.09230769
## 160 2020-10-09 new_deaths 746 816 70 0.08962868
## 161 2020-06-22 new_deaths 583 533 50 0.08960573
## 162 2020-08-23 new_deaths 756 692 64 0.08839779
## 163 2021-06-18 new_deaths 229 210 19 0.08656036
## 164 2020-10-01 new_deaths 705 768 63 0.08553971
## 165 2021-06-30 new_deaths 247 227 20 0.08438819
## 166 2020-10-10 new_deaths 656 603 53 0.08419380
## 167 2021-05-18 new_deaths 603 656 53 0.08419380
## 168 2020-09-03 new_deaths 877 954 77 0.08410705
## 169 2021-04-08 new_deaths 718 781 63 0.08405604
## 170 2020-09-15 new_deaths 793 862 69 0.08338369
## 171 2020-10-13 new_deaths 741 805 64 0.08279431
## 172 2020-08-24 new_deaths 779 718 61 0.08149633
## 173 2020-09-01 new_deaths 959 1040 81 0.08104052
## 174 2020-09-27 new_deaths 489 453 36 0.07643312
## 175 2021-05-25 new_deaths 493 532 39 0.07609756
## 176 2020-09-12 new_deaths 599 646 47 0.07550201
## 177 2020-08-31 new_deaths 688 638 50 0.07541478
## 178 2020-09-25 new_deaths 705 760 55 0.07508532
## 179 2021-06-28 new_deaths 208 193 15 0.07481297
## 180 2020-06-14 new_deaths 491 456 35 0.07391763
## 181 2020-08-18 new_deaths 1033 1112 79 0.07365967
## 182 2021-05-09 new_deaths 385 358 27 0.07267833
## 183 2020-09-29 new_deaths 862 927 65 0.07266629
## 184 2020-07-24 new_deaths 1337 1244 93 0.07206509
## 185 2021-04-05 new_deaths 406 378 28 0.07142857
## 186 2021-06-06 new_deaths 230 247 17 0.07127883
## 187 2020-07-17 new_deaths 1095 1020 75 0.07092199
## 188 2021-06-15 new_deaths 313 336 23 0.07087827
## 189 2020-10-07 new_deaths 791 849 58 0.07073171
## 190 2021-03-25 new_deaths 767 823 56 0.07044025
## 191 2021-07-01 new_deaths 220 236 16 0.07017544
## 192 2021-05-20 new_deaths 504 540 36 0.06896552
## 193 2020-10-25 new_deaths 653 610 43 0.06809184
## 194 2020-09-14 new_deaths 539 504 35 0.06711409
## 195 2021-03-14 new_deaths 635 594 41 0.06672091
## 196 2020-03-24 new_deaths 234 219 15 0.06622517
## 197 2020-07-15 new_deaths 1171 1096 75 0.06616674
## 198 2021-06-16 new_deaths 331 310 21 0.06552262
## 199 2021-03-30 new_deaths 726 775 49 0.06528981
## 200 2020-07-21 new_deaths 1335 1251 84 0.06496520
## 201 2021-03-04 new_deaths 1235 1316 81 0.06350451
## 202 2021-05-04 new_deaths 698 743 45 0.06245663
## 203 2020-08-14 new_deaths 1011 1076 65 0.06229037
## 204 2020-12-25 new_deaths 2491 2341 150 0.06208609
## 205 2020-07-11 new_deaths 881 828 53 0.06202458
## 206 2021-06-25 new_deaths 319 300 19 0.06138934
## 207 2020-10-21 new_deaths 1046 1112 66 0.06116775
## 208 2021-05-17 new_deaths 397 422 25 0.06105006
## 209 2020-08-25 new_deaths 976 1037 61 0.06060606
## 210 2021-04-26 new_deaths 496 527 31 0.06060606
## 211 2021-04-25 new_deaths 426 401 25 0.06045949
## 212 2020-08-01 new_deaths 1144 1077 67 0.06033318
## 213 2021-02-16 new_deaths 1595 1694 99 0.06020067
## 214 2020-03-25 new_deaths 312 294 18 0.05940594
## 215 2020-08-28 new_deaths 954 899 55 0.05936319
## 216 2021-05-12 new_deaths 655 695 40 0.05925926
## 217 2021-05-21 new_deaths 592 628 36 0.05901639
## 218 2021-05-26 new_deaths 497 527 30 0.05859375
## 219 2021-04-16 new_deaths 763 809 46 0.05852417
## 220 2020-06-21 new_deaths 406 383 23 0.05830165
## 221 2021-01-01 new_deaths 3181 3001 180 0.05823358
## 222 2021-02-19 new_deaths 2124 2251 127 0.05805714
## 223 2021-04-06 new_deaths 724 767 43 0.05767941
## 224 2021-06-02 new_deaths 473 501 28 0.05749487
## 225 2020-08-22 new_deaths 898 848 50 0.05727377
## 226 2021-06-10 new_deaths 379 401 22 0.05641026
## 227 2021-06-27 new_deaths 147 139 8 0.05594406
## 228 2020-08-20 new_deaths 975 922 53 0.05587770
## 229 2021-04-17 new_deaths 576 609 33 0.05569620
## 230 2021-05-02 new_deaths 409 387 22 0.05527638
## 231 2021-04-07 new_deaths 734 695 39 0.05458362
## 232 2020-10-02 new_deaths 772 815 43 0.05419030
## 233 2021-03-09 new_deaths 1064 1122 58 0.05306496
## 234 2021-03-16 new_deaths 888 936 48 0.05263158
## 235 2020-06-29 new_deaths 537 510 27 0.05157593
## 236 2020-05-04 new_deaths 1342 1275 67 0.05120367
## 237 2020-07-22 new_deaths 1203 1143 60 0.05115090
## 238 2020-06-27 new_deaths 585 556 29 0.05083260
## 239 2021-06-11 new_deaths 343 326 17 0.05082212
## 240 2021-05-19 new_deaths 570 542 28 0.05035971
## 241 2020-01-22 new_cases 7 33 26 1.30000000
## 242 2020-11-07 new_cases 131834 95671 36163 0.31790950
## 243 2020-02-03 new_cases 26 32 6 0.20689655
## 244 2021-07-29 new_cases 100649 83462 17187 0.18670259
## 245 2020-08-31 new_cases 36951 31737 5214 0.15181691
## 246 2021-07-28 new_cases 96177 84435 11742 0.13002458
## 247 2021-07-26 new_cases 58108 51051 7057 0.12929763
## 248 2020-09-01 new_cases 39455 44538 5083 0.12103390
## 249 2021-07-27 new_cases 87616 77663 9953 0.12043877
## 250 2021-06-06 new_cases 10767 12103 1336 0.11683428
## 251 2021-07-06 new_cases 16896 15036 1860 0.11649756
## 252 2021-06-28 new_cases 10878 9695 1183 0.11500510
## 253 2021-04-12 new_cases 60857 54867 5990 0.10352217
## 254 2020-10-11 new_cases 43258 47898 4640 0.10180350
## 255 2021-07-20 new_cases 53311 48234 5077 0.09999508
## 256 2021-01-02 new_cases 202798 223976 21178 0.09924691
## 257 2021-03-01 new_cases 50563 45900 4663 0.09667956
## 258 2020-07-05 new_cases 41444 45432 3988 0.09180902
## 259 2020-12-26 new_cases 139548 151877 12329 0.08461182
## 260 2021-07-31 new_cases 93240 85965 7275 0.08119193
## 261 2020-05-28 new_cases 26010 24023 1987 0.07942758
## 262 2021-04-18 new_cases 47821 51724 3903 0.07841680
## 263 2020-10-04 new_cases 33811 36560 2749 0.07812877
## 264 2020-11-26 new_cases 153909 166180 12271 0.07667243
## 265 2021-05-22 new_cases 21532 23239 1707 0.07625472
## 266 2020-06-25 new_cases 52825 48970 3855 0.07574046
## 267 2021-07-12 new_cases 24442 22694 1748 0.07416836
## 268 2021-06-05 new_cases 14444 15543 1099 0.07329843
## 269 2020-10-10 new_cases 54240 50606 3634 0.06932072
## 270 2020-06-28 new_cases 42343 45369 3026 0.06899854
## 271 2020-07-11 new_cases 71056 66320 4736 0.06894945
## 272 2021-07-19 new_cases 37654 35205 2449 0.06722574
## 273 2021-04-11 new_cases 50023 53464 3441 0.06650111
## 274 2020-07-13 new_cases 54801 58407 3606 0.06370575
## 275 2021-02-28 new_cases 48788 51996 3208 0.06366090
## 276 2021-04-24 new_cases 51747 55068 3321 0.06218228
## 277 2020-07-15 new_cases 74637 70325 4312 0.05949145
## 278 2020-05-21 new_cases 26254 27823 1569 0.05802837
## 279 2021-02-13 new_cases 80777 85590 4813 0.05786003
## 280 2020-07-07 new_cases 61062 57638 3424 0.05769166
## 281 2020-08-09 new_cases 41266 43702 2436 0.05733923
## 282 2021-07-24 new_cases 61584 58168 3416 0.05705124
## 283 2021-06-11 new_cases 13729 14527 798 0.05648358
## 284 2021-02-15 new_cases 55513 52487 3026 0.05603704
## 285 2021-06-25 new_cases 14327 13571 756 0.05419743
## 286 2020-08-15 new_cases 44116 46541 2425 0.05349835
## 287 2021-07-23 new_cases 70211 66555 3656 0.05346358
## 288 2020-07-19 new_cases 58063 61214 3151 0.05283500
## 289 2020-10-21 new_cases 73061 69480 3581 0.05024519
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 FL tot_deaths 10350964 9728382 622582 0.062012179
## 2 PR tot_deaths 628681 609082 19599 0.031668421
## 3 KY tot_deaths 1851436 1839137 12299 0.006665090
## 4 MP tot_deaths 969 964 5 0.005173306
## 5 IN tot_deaths 3814159 3798160 15999 0.004203450
## 6 CA tot_deaths 16026588 15970217 56371 0.003523539
## 7 MS tot_deaths 2215491 2207767 7724 0.003492448
## 8 AL tot_deaths 3076064 3066125 9939 0.003236305
## 9 NM tot_deaths 1127229 1124549 2680 0.002380341
## 10 NC tot_deaths 3461659 3454115 7544 0.002181679
## 11 SC tot_deaths 2563894 2568618 4724 0.001840814
## 12 PR tot_cases 31613320 33334437 1721117 0.053000044
## 13 CO tot_cases 128884494 126439079 2445415 0.019155419
## 14 PA tot_cases 273389806 270659767 2730039 0.010035994
## 15 AL tot_cases 146850606 148145611 1295005 0.008779808
## 16 SC tot_cases 145874578 146346674 472096 0.003231086
## 17 FL tot_cases 578339105 576560229 1778876 0.003080573
## 18 MI tot_cases 242865368 242316039 549329 0.002264427
## 19 NE tot_cases 58248153 58158844 89309 0.001534427
## 20 RI tot_cases 36690249 36744702 54453 0.001483027
## 21 MP tot_cases 49822 49883 61 0.001223610
## 22 FL new_deaths 40667 39179 1488 0.037271748
## 23 MS new_deaths 7791 7544 247 0.032213890
## 24 KY new_deaths 7510 7348 162 0.021806434
## 25 GA new_deaths 21232 21698 466 0.021709760
## 26 AL new_deaths 11772 11542 230 0.019730634
## 27 NM new_deaths 4452 4414 38 0.008572073
## 28 NC new_deaths 13776 13670 106 0.007724259
## 29 TN new_deaths 12847 12758 89 0.006951767
## 30 IN new_deaths 14084 14012 72 0.005125285
## 31 PR new_deaths 2592 2585 7 0.002704269
## 32 CA new_deaths 64106 63942 164 0.002561539
## 33 SC new_deaths 9979 9958 21 0.002106636
## 34 MP new_cases 196 183 13 0.068601583
## 35 AL new_cases 600718 592417 8301 0.013914603
## 36 CO new_cases 578677 572616 6061 0.010529031
## 37 CA new_cases 4076069 4037808 38261 0.009431003
## 38 WA new_cases 479286 475881 3405 0.007129643
## 39 NC new_cases 1064245 1056699 7546 0.007115699
## 40 FL new_cases 2658193 2641696 16497 0.006225413
## 41 KY new_cases 488931 486115 2816 0.005776138
## 42 GA new_cases 1190936 1185594 5342 0.004495630
## 43 MI new_cases 1017675 1013112 4563 0.004493824
## 44 PA new_cases 1232900 1227519 5381 0.004374052
## 45 TN new_cases 903665 900418 3247 0.003599613
## 46 SD new_cases 125481 125216 265 0.002114106
## 47 SC new_cases 625173 623861 1312 0.002100824
## 48 PR new_cases 148020 147820 200 0.001352082
##
##
##
## Raw file for cdcDaily:
## Rows: 37,320
## Columns: 15
## $ date <date> 2021-02-12, 2021-03-01, 2020-08-22, 2020-08-12, 2020-0~
## $ state <chr> "UT", "CO", "AR", "AS", "HI", "AK", "TX", "OK", "TX", "~
## $ tot_cases <dbl> 359641, 438745, 56199, 0, 661, 71521, 1867163, 475578, ~
## $ conf_cases <dbl> 359641, 411869, NA, NA, NA, NA, NA, 373929, NA, 881626,~
## $ prob_cases <dbl> 0, 26876, NA, NA, NA, NA, NA, 101649, NA, 193571, NA, 4~
## $ new_cases <dbl> 1060, 677, 547, 0, 8, 235, 24010, 1028, 18811, 1755, 0,~
## $ pnew_case <dbl> 0, 60, 0, 0, 0, 0, 4196, 264, 3202, 168, 0, 0, 0, 197, ~
## $ tot_deaths <dbl> 1785, 5952, 674, 0, 17, 377, 33124, 7488, 23357, 34153,~
## $ conf_death <dbl> 1729, 5218, NA, NA, NA, NA, NA, 6379, NA, 28965, NA, 41~
## $ prob_death <dbl> 56, 734, NA, NA, NA, NA, NA, 1109, NA, 5188, NA, 167, N~
## $ new_deaths <dbl> 11, 1, 11, 0, 0, 0, 345, 8, 190, 20, 0, 13, 7, 8, 37, 0~
## $ pnew_death <dbl> 2, 0, 0, 0, 0, 0, 0, 2, 0, -7, 0, 1, NA, 0, 7, NA, 0, 0~
## $ created_at <chr> "02/13/2021 02:50:08 PM", "03/01/2021 12:00:00 AM", "08~
## $ consent_cases <chr> "Agree", "Agree", "Not agree", NA, "Not agree", "N/A", ~
## $ consent_deaths <chr> "Agree", "Agree", "Not agree", NA, "Not agree", "N/A", ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/CDC_h_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses on_hand_supply_therapeutic_b_bamlanivimab_courses on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses previous_week_therapeutic_a_casirivimab_imdevimab_courses_used previous_week_therapeutic_b_bamlanivimab_courses_used previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used icu_patients_confirmed_influenza icu_patients_confirmed_influenza_coverage previous_day_admission_influenza_confirmed previous_day_admission_influenza_confirmed_coverage previous_day_deaths_covid_and_influenza previous_day_deaths_covid_and_influenza_coverage previous_day_deaths_influenza previous_day_deaths_influenza_coverage total_patients_hospitalized_confirmed_influenza total_patients_hospitalized_confirmed_influenza_and_covid total_patients_hospitalized_confirmed_influenza_and_covid_coverage total_patients_hospitalized_confirmed_influenza_coverage
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 66
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference: AS
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-07-02 inp 18525 17110 1415 0.07941630
## 2 2021-07-31 inp 51524 48807 2717 0.05416073
## 3 2020-08-02 hosp_ped 4781 4498 283 0.06099795
## 4 2021-07-31 hosp_ped 1313 1247 66 0.05156250
## 5 2021-07-02 hosp_ped 696 662 34 0.05007364
## 6 2021-07-02 hosp_adult 17829 16448 1381 0.08057881
## 7 2021-07-31 hosp_adult 50211 47560 2651 0.05422876
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 GA inp 1164508 1159071 5437 0.004679849
## 2 NC inp 675965 673836 2129 0.003154539
## 3 AL inp 544531 542977 1554 0.002857910
## 4 IA inp 182179 182542 363 0.001990563
## 5 PR inp 153806 153545 261 0.001698384
## 6 TX inp 2867610 2864342 3268 0.001140275
## 7 ME inp 46767 46818 51 0.001089918
## 8 NH hosp_ped 330 285 45 0.146341463
## 9 ME hosp_ped 573 516 57 0.104683196
## 10 VI hosp_ped 31 33 2 0.062500000
## 11 KS hosp_ped 1755 1819 64 0.035814214
## 12 KY hosp_ped 5588 5760 172 0.030313712
## 13 ID hosp_ped 1422 1389 33 0.023479189
## 14 AR hosp_ped 6228 6372 144 0.022857143
## 15 NM hosp_ped 3206 3279 73 0.022513493
## 16 MA hosp_ped 5244 5129 115 0.022172949
## 17 NV hosp_ped 2237 2190 47 0.021233341
## 18 DE hosp_ped 1790 1754 36 0.020316027
## 19 VA hosp_ped 6990 6870 120 0.017316017
## 20 MS hosp_ped 4223 4295 72 0.016905377
## 21 NJ hosp_ped 9458 9311 147 0.015664127
## 22 SC hosp_ped 2881 2843 38 0.013277428
## 23 UT hosp_ped 3309 3337 28 0.008426121
## 24 AL hosp_ped 8448 8385 63 0.007485297
## 25 LA hosp_ped 3659 3676 17 0.004635310
## 26 PA hosp_ped 20751 20656 95 0.004588596
## 27 IL hosp_ped 20634 20548 86 0.004176582
## 28 OH hosp_ped 28226 28343 117 0.004136541
## 29 TN hosp_ped 8191 8223 32 0.003899111
## 30 AZ hosp_ped 11929 11884 45 0.003779448
## 31 NC hosp_ped 11220 11258 38 0.003381084
## 32 MO hosp_ped 16846 16901 55 0.003259549
## 33 GA hosp_ped 22986 22921 65 0.002831812
## 34 IN hosp_ped 7398 7418 20 0.002699784
## 35 WV hosp_ped 2294 2289 5 0.002181977
## 36 CT hosp_ped 2467 2462 5 0.002028809
## 37 PR hosp_ped 11845 11868 23 0.001939864
## 38 OR hosp_ped 3144 3150 6 0.001906578
## 39 FL hosp_ped 56811 56703 108 0.001902849
## 40 OK hosp_ped 12413 12436 23 0.001851181
## 41 WA hosp_ped 4613 4621 8 0.001732727
## 42 IA hosp_ped 2445 2441 4 0.001637331
## 43 RI hosp_ped 1454 1452 2 0.001376462
## 44 GA hosp_adult 855243 849991 5252 0.006159858
## 45 AL hosp_adult 463636 462110 1526 0.003296801
## 46 ME hosp_adult 38368 38476 108 0.002810890
## 47 IA hosp_adult 153462 153829 367 0.002388615
## 48 PR hosp_adult 120751 120467 284 0.002354716
## 49 NC hosp_adult 571992 570956 1036 0.001812856
## 50 VI hosp_adult 1703 1700 3 0.001763150
## 51 KY hosp_adult 310452 310043 409 0.001318302
## 52 TX hosp_adult 2413855 2411369 2486 0.001030418
##
##
##
## Raw file for cdcHosp:
## Rows: 31,223
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~
##
## No file has been downloaded, will use existing file: ./RInputFiles/Coronavirus/vaxData_downloaded_211006.csv##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Additional_Doses Additional_Doses_Vax_Pct Additional_Doses_18Plus Additional_Doses_18Plus_Vax_Pct Additional_Doses_50Plus Additional_Doses_50Plus_Vax_Pct Additional_Doses_65Plus Additional_Doses_65Plus_Vax_Pct Additional_Doses_Moderna Additional_Doses_Pfizer Additional_Doses_Janssen Additional_Doses_Unk_Manuf
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 63
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 19,208
## Columns: 81
## $ date <date> 2021-10-05, 2021-10-05, 2021-1~
## $ MMWR_week <dbl> 40, 40, 40, 40, 40, 40, 40, 40,~
## $ state <chr> "VA", "NV", "RP", "NE", "MI", "~
## $ Distributed <dbl> 12869745, 4045120, 33090, 25822~
## $ Distributed_Janssen <dbl> 573200, 199900, 3800, 116400, 6~
## $ Distributed_Moderna <dbl> 4833340, 1475340, 22900, 979980~
## $ Distributed_Pfizer <dbl> 7463205, 2369880, 6390, 1485870~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 150779, 131328, 184788, 133490,~
## $ Distributed_Per_100k_12Plus <dbl> 176130, 154288, 216388, 159654,~
## $ Distributed_Per_100k_18Plus <dbl> 192815, 169428, 237255, 177068,~
## $ Distributed_Per_100k_65Plus <dbl> 947067, 815594, 1090640, 826431~
## $ vxa <dbl> 11024264, 3443155, 31240, 22012~
## $ Administered_12Plus <dbl> 10992921, 3443100, 31240, 22009~
## $ Administered_18Plus <dbl> 10178448, 3225317, 28652, 20476~
## $ Administered_65Plus <dbl> 2517946, 850914, 3551, 600142, ~
## $ Administered_Janssen <dbl> 399119, 151785, 2333, 79234, 37~
## $ Administered_Moderna <dbl> 3963920, 1219801, 24444, 817158~
## $ Administered_Pfizer <dbl> 6655345, 2071438, 4463, 1300158~
## $ Administered_Unk_Manuf <dbl> 5880, 131, 0, 4746, 1616, 1270,~
## $ Administered_Fed_LTC <dbl> 217074, 74066, 0, 60654, 295799~
## $ Administered_Fed_LTC_Residents <dbl> 109722, 16660, 0, 27041, 141181~
## $ Administered_Fed_LTC_Staff <dbl> 80551, 14302, 0, 23762, 86738, ~
## $ Administered_Fed_LTC_Unk <dbl> 26801, 43104, 0, 9851, 67880, 1~
## $ Administered_Fed_LTC_Dose1 <dbl> 121472, 50270, 0, 37443, 182161~
## $ Administered_Fed_LTC_Dose1_Residents <dbl> 58921, 9149, 0, 16713, 81899, 2~
## $ Administered_Fed_LTC_Dose1_Staff <dbl> 44628, 7977, 0, 15423, 51531, 1~
## $ Administered_Fed_LTC_Dose1_Unk <dbl> 17923, 33144, 0, 5307, 48731, 9~
## $ Admin_Per_100k <dbl> 129158, 111785, 174457, 113797,~
## $ Admin_Per_100k_12Plus <dbl> 150444, 131326, 204290, 136077,~
## $ Admin_Per_100k_18Plus <dbl> 152494, 135091, 205435, 140410,~
## $ Admin_Per_100k_65Plus <dbl> 185292, 171565, 117040, 192071,~
## $ Recip_Administered <dbl> 11015106, 3407219, 31501, 22079~
## $ Administered_Dose1_Recip <dbl> 5877501, 1886012, 17827, 115491~
## $ Administered_Dose1_Pop_Pct <dbl> 68.9, 61.2, 99.6, 59.7, 57.4, 6~
## $ Administered_Dose1_Recip_12Plus <dbl> 5859164, 1885912, 17827, 115463~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 80.2, 71.9, 99.9, 71.4, 66.7, 7~
## $ Administered_Dose1_Recip_18Plus <dbl> 5430306, 1763229, 16486, 107349~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 81.4, 73.9, 99.9, 73.6, 68.9, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 1285517, 437932, 1785, 287680, ~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.6, 88.3, 58.8, 92.1, 89.0, 9~
## $ vxc <dbl> 5209839, 1575904, 15556, 106388~
## $ vxcpoppct <dbl> 61.0, 51.2, 86.9, 55.0, 52.7, 5~
## $ Series_Complete_12Plus <dbl> 5197778, 1575865, 15556, 106379~
## $ Series_Complete_12PlusPop_Pct <dbl> 71.1, 60.1, 99.9, 65.8, 61.2, 6~
## $ vxcgte18 <dbl> 4823813, 1484340, 14304, 992233~
## $ vxcgte18pct <dbl> 72.3, 62.2, 99.9, 68.0, 63.3, 6~
## $ vxcgte65 <dbl> 1167111, 385863, 1705, 275394, ~
## $ vxcgte65pct <dbl> 85.9, 77.8, 56.2, 88.1, 84.1, 8~
## $ Series_Complete_Janssen <dbl> 390589, 149312, 2338, 79114, 37~
## $ Series_Complete_Moderna <dbl> 1826509, 552554, 11582, 385208,~
## $ Series_Complete_Pfizer <dbl> 2990444, 874003, 1636, 598304, ~
## $ Series_Complete_Unk_Manuf <dbl> 2297, 35, 0, 1260, 868, 446, 3,~
## $ Series_Complete_Janssen_12Plus <dbl> 390495, 149308, 2338, 79096, 37~
## $ Series_Complete_Moderna_12Plus <dbl> 1826340, 552550, 11582, 385188,~
## $ Series_Complete_Pfizer_12Plus <dbl> 2978651, 873972, 1636, 598257, ~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 2292, 35, 0, 1258, 867, 446, 3,~
## $ Series_Complete_Janssen_18Plus <dbl> 389060, 149270, 2338, 79040, 37~
## $ Series_Complete_Moderna_18Plus <dbl> 1821019, 552455, 11582, 385060,~
## $ Series_Complete_Pfizer_18Plus <dbl> 2611548, 782581, 384, 526920, 2~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 2186, 34, 0, 1213, 810, 398, 3,~
## $ Series_Complete_Janssen_65Plus <dbl> 67735, 23902, 227, 6449, 66933,~
## $ Series_Complete_Moderna_65Plus <dbl> 549368, 180305, 1462, 134092, 7~
## $ Series_Complete_Pfizer_65Plus <dbl> 549284, 181636, 16, 134058, 675~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 724, 20, 0, 795, 509, 207, 3, 1~
## $ Series_Complete_FedLTC <dbl> 88584, 23917, 0, 23177, 113757,~
## $ Series_Complete_FedLTC_Residents <dbl> 45746, 7416, 0, 10273, 58777, 2~
## $ Series_Complete_FedLTC_Staff <dbl> 32916, 6273, 0, 8304, 34714, 11~
## $ Series_Complete_FedLTC_Unknown <dbl> 9922, 10228, 0, 4600, 20266, 68~
## $ Additional_Doses <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_18Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_50Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_65Plus <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0~
## $ Additional_Doses_Moderna <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Pfizer <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Janssen <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Additional_Doses_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.08e+10 2.08e+8 4.37e+7 693668 36698
## 2 after 1.08e+10 2.07e+8 4.35e+7 690216 31722
## 3 pctchg 4.32e- 3 4.21e-3 4.68e-3 0.00498 0.136
##
##
## Processed for cdcDaily:
## Rows: 31,722
## Columns: 6
## $ date <date> 2021-02-12, 2021-03-01, 2020-08-22, 2020-06-05, 2021-07-27~
## $ state <chr> "UT", "CO", "AR", "HI", "AK", "TX", "OK", "TX", "GA", "MA",~
## $ tot_cases <dbl> 359641, 438745, 56199, 661, 71521, 1867163, 475578, 1236648~
## $ tot_deaths <dbl> 1785, 5952, 674, 17, 377, 33124, 7488, 23357, 13, 17427, 21~
## $ new_cases <dbl> 1060, 677, 547, 8, 235, 24010, 1028, 18811, 115, 1598, 1195~
## $ new_deaths <dbl> 11, 1, 11, 0, 0, 345, 8, 190, 7, 8, 37, 0, 4, 15, 31, 1, 4,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.36e+7 2.76e+7 605791 31223
## 2 after 3.35e+7 2.75e+7 592135 30045
## 3 pctchg 5.36e-3 5.26e-3 0.0225 0.0377
##
##
## Processed for cdcHosp:
## Rows: 30,045
## Columns: 5
## $ date <date> 2021-02-02, 2021-01-30, 2021-01-28, 2021-01-26, 2021-01-21~
## $ state <chr> "LA", "DE", "IA", "DE", "ID", "HI", "MT", "IA", "NV", "NH",~
## $ inp <dbl> 1276, 352, 397, 368, 248, 119, 164, 548, 1763, 294, 434, 23~
## $ hosp_adult <dbl> 1260, 347, 392, 362, 243, 117, 161, 543, 1755, 291, 432, 23~
## $ hosp_ped <dbl> 16, 5, 5, 6, 5, 2, 3, 5, 8, 3, 2, 1, 31, 3, 7, 10, 8, 5, 9,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.32e+11 5.71e+10 509604 1.70e+10 867745. 5.49e+10 621075.
## 2 after 6.30e+10 2.76e+10 429668. 8.24e+ 9 788951. 2.65e+10 530761.
## 3 pctchg 5.23e- 1 5.16e- 1 0.157 5.16e- 1 0.0908 5.17e- 1 0.145
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 15,096
## Columns: 9
## $ date <date> 2021-10-05, 2021-10-05, 2021-10-05, 2021-10-05, 2021-10-0~
## $ state <chr> "VA", "NV", "NE", "MI", "KS", "WV", "TN", "MA", "GA", "AL"~
## $ vxa <dbl> 11024264, 3443155, 2201296, 10788794, 3123280, 1559989, 69~
## $ vxc <dbl> 5209839, 1575904, 1063886, 5260265, 1501221, 726445, 31367~
## $ vxcpoppct <dbl> 61.0, 51.2, 55.0, 52.7, 51.5, 40.5, 45.9, 68.2, 45.7, 43.1~
## $ vxcgte65 <dbl> 1167111, 385863, 275394, 1484308, 403394, 262042, 896684, ~
## $ vxcgte65pct <dbl> 85.9, 77.8, 88.1, 84.1, 84.8, 71.4, 78.4, 89.0, 76.5, 75.3~
## $ vxcgte18 <dbl> 4823813, 1484340, 992233, 4968064, 1404254, 695313, 298538~
## $ vxcgte18pct <dbl> 72.3, 62.2, 68.0, 63.3, 63.5, 48.5, 56.1, 78.9, 56.4, 52.7~
##
## Integrated per capita data file:
## Rows: 31,935
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211006)Additional analysis of the resulting data is conducted:
hospAge_211006 <- cdc_daily_211006$dfRaw$cdcHosp %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped", "0-17", stringr::str_replace_all(string=name, pattern=".*_", replacement="")),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
hospAge_211006## # A tibble: 562,014 x 8
## state date name value confSusp adultPed age div
## <chr> <date> <chr> <dbl> <chr> <chr> <chr> <chr>
## 1 LA 2021-02-02 previous_day_admiss~ 1 confirm~ adult 0-19 West Sou~
## 2 LA 2021-02-02 previous_day_admiss~ 8 confirm~ adult 20-29 West Sou~
## 3 LA 2021-02-02 previous_day_admiss~ 7 confirm~ adult 30-39 West Sou~
## 4 LA 2021-02-02 previous_day_admiss~ 20 confirm~ adult 40-49 West Sou~
## 5 LA 2021-02-02 previous_day_admiss~ 23 confirm~ adult 50-59 West Sou~
## 6 LA 2021-02-02 previous_day_admiss~ 26 confirm~ adult 60-69 West Sou~
## 7 LA 2021-02-02 previous_day_admiss~ 48 confirm~ adult 70-79 West Sou~
## 8 LA 2021-02-02 previous_day_admiss~ 30 confirm~ adult 80+ West Sou~
## 9 LA 2021-02-02 previous_day_admiss~ 0 suspect~ adult 0-19 West Sou~
## 10 LA 2021-02-02 previous_day_admiss~ 2 suspect~ adult 20-29 West Sou~
## # ... with 562,004 more rowsdfPivot_211006 <- makeCaseHospDeath(dfHosp=hospAge_211006, dfCaseDeath=cdc_daily_211006$dfPerCapita)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date namedfPivot_211006## # A tibble: 349,149 x 4
## state date name value
## <chr> <date> <chr> <dbl>
## 1 AL 2020-01-01 0-19 NA
## 2 AL 2020-01-01 20-59 NA
## 3 AL 2020-01-01 60+ NA
## 4 HI 2020-01-01 0-19 NA
## 5 HI 2020-01-01 20-59 NA
## 6 HI 2020-01-01 60+ NA
## 7 IN 2020-01-01 0-19 NA
## 8 IN 2020-01-01 20-59 NA
## 9 IN 2020-01-01 60+ NA
## 10 LA 2020-01-01 0-19 NA
## # ... with 349,139 more rows# Plot for overall trends by age group
p1 <- hospAge_211006 %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-08-01") %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Sep 2021)",
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 row(s) containing missing values (geom_path).
p1 + geom_col(aes(fill=ageBucket), position="stack") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
p1 + geom_col(aes(fill=ageBucket), position="fill") +
scale_color_discrete("Age\nbucket")## Warning: Removed 24 rows containing missing values (position_stack).
# Plot for overall trends by age group
hospAge_211006 %>%
filter(state %in% state.abb, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= "2020-06-01") %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title="Hospital admissions for COVID by age bucket (Aug 2020 - Sep 2021)"
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")## Warning: Removed 18 row(s) containing missing values (geom_path).
onePageCFRPlot(dfPivot_211006, keyState="FL", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="LA", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="OR", minDate="2020-08-01")
onePageCFRPlot(dfPivot_211006, keyState="HI", minDate="2020-08-01")
The process is converted to functional form:
createBurdenPivot <- function(lst,
dataThru,
minDatePlot="2020-08-01",
plotByState=c(state.abb, "DC")
) {
# FUNCTION ARGUMENTS:
# lst: a processed list that includes sub-component $dfRaw$cdcHosp
# dataThru: character string to be used for 'data through'; most commonly MMM-YY
# minDatePlot: starting date for plots
# plotByState: states to be facetted for plot of hospitaliztions by age (FALSE means do not create plot)
# Convert minDatePlot to Date if passed as character
if ("character" %in% class(minDatePlot)) minDatePlot <- as.Date(minDatePlot)
# Create the hospitalized by age data
hospAge <- lst[["dfRaw"]][["cdcHosp"]] %>%
select(state,
date,
grep(x=names(.), pattern="ed_\\d.*[9+]$", value=TRUE),
grep(x=names(.), pattern="pediatric.*ed$", value=TRUE)
) %>%
pivot_longer(-c(state, date)) %>%
mutate(confSusp=ifelse(grepl(x=name, pattern="confirmed"), "confirmed", "suspected"),
adultPed=ifelse(grepl(x=name, pattern="adult"), "adult", "ped"),
age=ifelse(adultPed=="ped",
"0-17",
stringr::str_replace_all(string=name, pattern=".*_", replacement="")
),
age=ifelse(age %in% c("0-17", "18-19"), "0-19", age),
div=as.character(state.division)[match(state, state.abb)]
)
# Create the pivoted burden data
dfPivot <- makeCaseHospDeath(dfHosp=hospAge, dfCaseDeath=lst[["dfPerCapita"]])
# Plot for overall trends by age group
p1 <- hospAge %>%
filter(state %in% c(state.abb, "DC"), !is.na(value)) %>%
mutate(ageBucket=age) %>%
group_by(date, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
arrange(date) %>%
group_by(ageBucket) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")"),
subtitle="50 states and DC (includes confirmed and suspected from CDC data)"
) +
lims(y=c(0, NA))
# Create three main plots of hospitalized by age data
print(p1 + geom_line(aes(group=ageBucket, color=ageBucket), size=1) + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="stack") + scale_color_discrete("Age\nbucket"))
print(p1 + geom_col(aes(fill=ageBucket), position="fill") + scale_color_discrete("Age\nbucket"))
# Plot for trends by state and age group
if (!isFALSE(plotByState)) {
p2 <- hospAge %>%
filter(state %in% plotByState, !is.na(value)) %>%
mutate(ageBucket=ifelse(age >= "60", "60+", ifelse(age=="0-19", "0-19", "20-59"))) %>%
group_by(date, state, ageBucket) %>%
summarize(value=sum(value), .groups="drop") %>%
group_by(ageBucket, state) %>%
mutate(value7=zoo::rollmean(value, k=7, fill=NA)) %>%
filter(date >= minDatePlot) %>%
ggplot(aes(x=date, y=value7)) +
geom_line(aes(color=ageBucket, group=ageBucket)) +
scale_color_discrete("Age\nbucket") +
labs(x=NULL,
y="Confirmed or suspected COVID admissions (rolling-7 mean)",
title=paste0("Hospital admissions for COVID by age bucket (Aug 2020 - ", dataThru, ")")
) +
lims(y=c(0, NA)) +
facet_wrap(~state, scales="free_y")
print(p2)
}
# Return key data (do not return plot objects)
list(hospAge=hospAge, dfPivot=dfPivot)
}
burdenPivotList_211006 <- createBurdenPivot(cdc_daily_211006, dataThru="Sep 2021")## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
identical(burdenPivotList_211006$dfPivot, dfPivot_211006)## [1] TRUEidentical(burdenPivotList_211006$hospAge, hospAge_211006)## [1] TRUEPlots for burden growth are also updated:
# Create data
cdcBurdenGrowth_211006 <- cdc_daily_211006$dfPerCapita %>%
filter(date %in% c(as.Date(max(date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for cases
p1 <- cdcBurdenGrowth_211006 %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=c("2021-10-03"="2021-10-03",
"2021-04-03"="2021-04-03",
"2020-10-02"="2020-10-02"
),
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date"
)
# Run for deaths
p2 <- cdcBurdenGrowth_211006 %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=c("2021-10-03"="2021-10-03",
"2021-04-03"="2021-04-03",
"2020-10-02"="2020-10-02"
),
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The burden plot is converted to functional form:
cumulativeBurdenPlot <- function(lst,
keyStates=c(state.abb, "DC"),
keyDates=NULL,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file containing dfPerCapita
# keyStates: states to include in the plot
# keyDates: dates to include in the burden plot
# NULL means default to max(date)-2 from current, 6 months ago, 12 months ago)
# ...: other arguments to pass to tempStackPlot(), most commonly colorVector
# Get the list of key dates
if (is.null(keyDates)) {
keyDates <- as.Date(max(lst[["dfPerCapita"]]$date)-2-lubridate::dmonths(c(0, 6, 12)), origin="1970-01-01")
}
# Convert to date if needed
if (!("Date" %in% class(keyDates))) keyDates <- as.Date(keyDates)
# Create data filtered for keyDates and keyStates
burdenGrowth <- lst[["dfPerCapita"]] %>%
filter(date %in% all_of(keyDates),
state %in% all_of(keyStates)
)
# Create the naming vector for tempStackPlot
vecName <- as.character(keyDates) %>% purrr::set_names(as.character(keyDates))
# Create plot for cases
p1 <- burdenGrowth %>%
select(state, date, tcpm) %>%
mutate(tcpm=round(tcpm/1000)) %>%
pivot_wider(state, names_from="date", values_from="tcpm") %>%
tempStackPlot(yVars=vecName,
yLab="Cumulative cases per thousand",
plotTitle="Evolution of cumulative cases per thousand by state",
addSuffix="",
scaleName="Date",
...
)
# Create plot for deaths
p2 <- burdenGrowth %>%
select(state, date, tdpm) %>%
mutate(tdpm=round(tdpm)) %>%
pivot_wider(state, names_from="date", values_from="tdpm") %>%
tempStackPlot(yVars=vecName,
yLab="Cumulative deaths per million",
plotTitle="Evolution of cumulative deaths per million by state",
addSuffix="",
scaleName="Date",
...
)
# Print the plots
gridExtra::grid.arrange(p1, p2, nrow=1)
# Return the burden data
burdenGrowth
}
# Run with general defaults
cumulativeBurdenPlot(cdc_daily_211006)
## # A tibble: 153 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-10-02 AK 8170 73 141 2 39 38
## 2 2020-10-02 AL 165793 4018 868 18 939 925
## 3 2020-10-02 AR 85779 1391 958 7 582 559
## 4 2020-10-02 AZ 219878 5693 552 19 730 585
## 5 2020-10-02 CA 845804 17056 3786 41 3205 3063
## 6 2020-10-02 CO 73614 2057 734 3 336 317
## 7 2020-10-02 CT 58297 4513 555 2 215 214
## 8 2020-10-02 DC 15423 629 65 1 151 136
## 9 2020-10-02 DE 21221 645 174 3 121 121
## 10 2020-10-02 FL 703540 16653 2758 65 2625 2595
## # ... with 143 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl># Run for specified states and dates
cumulativeBurdenPlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-03-31", "2020-09-30", "2020-03-31")
)
## # A tibble: 64 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-03-31 AL 3027 49 211 8 191 NA
## 2 2020-03-31 AR 560 8 56 1 153 NA
## 3 2020-03-31 DE 414 10 70 3 51 NA
## 4 2020-03-31 FL 6516 178 903 31 1251 NA
## 5 2020-03-31 GA 4236 130 1012 27 483 NA
## 6 2020-03-31 KY 841 29 130 7 154 NA
## 7 2020-03-31 LA 5237 239 1212 54 776 NA
## 8 2020-03-31 MD 1660 44 0 8 300 NA
## 9 2020-03-31 MS 2242 153 151 14 131 NA
## 10 2020-03-31 NC 2314 13 210 2 100 NA
## # ... with 54 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl># Pass a color vector
cumulativeBurdenPlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-06-30", "2021-03-31",
"2020-12-31", "2020-09-30", "2020-06-30"
),
colorVector=c("lightblue", "grey", "green", "orange", "pink", "black")
)
## # A tibble: 96 x 34
## date state tot_cases tot_deaths new_cases new_deaths inp hosp_adult
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2020-06-30 AL 45865 1344 1536 23 973 NA
## 2 2020-06-30 AR 20777 270 520 5 414 NA
## 3 2020-06-30 DE 11728 509 40 2 103 NA
## 4 2020-06-30 FL 156228 3841 6527 53 7008 NA
## 5 2020-06-30 GA 81677 2805 1976 21 1659 NA
## 6 2020-06-30 KY 17073 684 267 6 579 NA
## 7 2020-06-30 LA 58095 3221 1014 22 808 499
## 8 2020-06-30 MD 67918 3316 359 13 862 NA
## 9 2020-06-30 MS 32836 1411 807 19 883 NA
## 10 2020-06-30 NC 71767 1446 2522 17 1029 NA
## # ... with 86 more rows, and 26 more variables: hosp_ped <dbl>, vxa <dbl>,
## # vxc <dbl>, vxcpoppct <dbl>, vxcgte65 <dbl>, vxcgte65pct <dbl>,
## # vxcgte18 <dbl>, vxcgte18pct <dbl>, tcpm <dbl>, tdpm <dbl>, cpm <dbl>,
## # dpm <dbl>, hpm <dbl>, ahpm <dbl>, phpm <dbl>, vxapm <dbl>, vxcpm <dbl>,
## # tcpm7 <dbl>, tdpm7 <dbl>, cpm7 <dbl>, dpm7 <dbl>, hpm7 <dbl>, ahpm7 <dbl>,
## # phpm7 <dbl>, vxapm7 <dbl>, vxcpm7 <dbl>Plots for vaccination are also updated:
# Run for fully vaccinated
tempStackPlot(cdc_daily_211006$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle="Fully vaccinated by age cohort and state (as of early-October 2021)"
)
# Run for first dose
tempStackPlot(cdc_daily_211006$dfRaw$vax %>% filter(date==max(date), state %in% c(state.abb, "DC")),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle="First-dose vaccinated by age cohort and state (as of early-October 2021)"
)
# Create data
cdcVaxGrowth_211006 <- cdc_daily_211006$dfRaw$vax %>%
filter(date %in% c(as.Date(max(date)-lubridate::dmonths(c(0, 3, 6)), origin="1970-01-01")),
state %in% c(state.abb, "DC")
)
# Run for fully vaccinated
p1 <- cdcVaxGrowth_211006 %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=c("2021-10-05"="2021-10-05",
"2021-07-05"="2021-07-05",
"2021-04-05"="2021-04-05"
),
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state"
)
p2 <- cdcVaxGrowth_211006 %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=c("2021-10-05"="2021-10-05",
"2021-07-05"="2021-07-05",
"2021-04-05"="2021-04-05"
),
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p1 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state"
)
p2 <- cdcVaxGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=c("2021-08-15"="2021-08-15",
"2021-06-15"="2021-06-15",
"2021-04-15"="2021-04-15"
),
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state"
)
gridExtra::grid.arrange(p1, p2, nrow=1)
The process is converted to functional form:
cumulativeVaccinePlot <- function(lst,
keyStates=c(state.abb, "DC"),
keyDates=NULL,
returnData=FALSE,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file containing dfPerCapita
# keyStates: states to include in the plot
# keyDates: dates to include in the burden plot
# NULL means default to max(date)-2 from current, 6 months ago, 12 months ago)
# returnData: boolean, should the data be returned?
# ...: other arguments to pass to tempStackPlot(), most commonly colorVector
# Get the list of key dates
if (is.null(keyDates)) {
keyDates <- as.Date(max(lst[["dfRaw"]][["vax"]]$date)-2-lubridate::dmonths(c(0, 3, 6)),
origin="1970-01-01"
)
}
# Convert to date if needed
if (!("Date" %in% class(keyDates))) keyDates <- as.Date(keyDates)
# Chart for fully vaccinated by state
p5 <- tempStackPlot(lst[["dfRaw"]][["vax"]] %>% filter(date==max(keyDates), state %in% keyStates),
yVars=c("vxcgte65pct"="65+",
"vxcgte18pct"="18+",
"vxcpoppct"="All"
),
yLab="% Fully vaccinated",
plotTitle=paste0("Fully vaccinated by age cohort and state\n(as of ", max(keyDates), ")"),
makeDotPlot=TRUE,
yLims = c(0, 105)
)
# Run for first dose
p6 <- tempStackPlot(lst[["dfRaw"]][["vax"]] %>% filter(date==max(keyDates), state %in% keyStates),
yVars=c("Administered_Dose1_Recip_65PlusPop_Pct"="65+",
"Administered_Dose1_Recip_18PlusPop_Pct"="18+",
"Administered_Dose1_Pop_Pct"="All"
),
yLab="% Receiving First Dose",
plotTitle=paste0("First-dose vaccinated by age cohort and state\n(as of ",
max(keyDates),
")"
),
makeDotPlot=TRUE,
yLims=c(0, 105)
)
gridExtra::grid.arrange(p5, p6, nrow=1)
# Create data filtered for keyDates and keyStates
burdenGrowth <- lst[["dfRaw"]][["vax"]] %>%
filter(date %in% all_of(keyDates),
state %in% all_of(keyStates)
)
# Create the naming vector for tempStackPlot
vecName <- as.character(keyDates) %>% purrr::set_names(as.character(keyDates))
# Run for fully vaccinated
p1 <- burdenGrowth %>%
select(state, date, vxcpoppct) %>%
pivot_wider(state, names_from="date", values_from="vxcpoppct") %>%
tempStackPlot(yVars=vecName,
yLab="% Fully Vaccinated (all population)",
plotTitle="Evolution of fully vaccinated rate by state",
...
)
p2 <- burdenGrowth %>%
select(state, date, vxcgte65pct) %>%
pivot_wider(state, names_from="date", values_from="vxcgte65pct") %>%
tempStackPlot(yVars=vecName,
yLab="% Fully Vaccinated (65+)",
plotTitle="Evolution of fully vaccinated rate by state",
...
)
gridExtra::grid.arrange(p1, p2, nrow=1)
# Run for first dose
p3 <- burdenGrowth %>%
select(state, date, Administered_Dose1_Pop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Pop_Pct") %>%
tempStackPlot(yVars=vecName,
yLab="% First-dose (all population)",
plotTitle="Evolution of first dose rate by state",
...
)
p4 <- burdenGrowth %>%
select(state, date, Administered_Dose1_Recip_65PlusPop_Pct) %>%
pivot_wider(state, names_from="date", values_from="Administered_Dose1_Recip_65PlusPop_Pct") %>%
tempStackPlot(yVars=vecName,
yLab="% First-dose (65+)",
plotTitle="Evolution of first dose rate by state",
...
)
gridExtra::grid.arrange(p3, p4, nrow=1)
# Return the burden data
if(isTRUE(returnData)) burdenGrowth
}
# Run with general defaults
cumulativeVaccinePlot(cdc_daily_211006)
# Run for specified states and dates
cumulativeVaccinePlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-06-30", "2021-03-31", "2020-12-31")
)
# Pass a color vector
cumulativeVaccinePlot(cdc_daily_211006,
keyStates=state.abb[state.region=="South"],
keyDates=c("2021-09-30", "2021-08-31", "2021-07-31",
"2021-06-30", "2021-05-31", "2021-04-30"
),
colorVector=c("lightblue", "grey", "green", "orange", "pink", "black")
)
The functions are integrated for a post-processing capability:
postProcessCDCDaily <- function(lst,
dataThruLabel,
keyStates=c(state.abb, "DC"),
keyDatesBurden=NULL,
keyDatesVaccine=NULL,
returnData=FALSE,
...
) {
# FUNCTION ARGUMENTS:
# lst: a processed list file from readRunCDCDaily
# dataThruLabel: label for when the hospital data are through
# keyStates: the list of states to be plotted (burden data will be created for all states)
# keyDatesBurden: key dates to use for the burden plots (NULL means generate automatically)
# keyDatesVaccine: key dates to use for the vaccine plots (NULL means generate automatically)
# returnData: should the pivoted data be returned?
# ...: other arguments passed through to cumulativeBurdenPlot()
# Create the burden data
burdenPivotList <- createBurdenPivot(lst, dataThru=dataThruLabel)
# Create the cumulative burden plots
cumulativeBurdenPlot(lst,
keyStates=keyStates,
keyDates=keyDatesBurden,
...
)
# Create the cumulative vaccines data
cumulativeVaccinePlot(lst,
keyStates=keyStates,
keyDates=keyDatesVaccine,
...
)
if (isTRUE(returnData)) return(burdenPivotList)
}
# Example code
postProcessCDCDaily(cdc_daily_211006,
dataThruLabel="Sep 2021",
keyStates=state.abb[state.region=="South"],
keyDatesBurden=c("2021-09-30", "2021-03-30", "2020-09-30", "2020-03-31"),
keyDatesVaccine=c("2021-09-30", "2021-07-31", "2021-05-31", "2021-03-31")
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
Data from the US Census is downloaded for age estimates by state. Per the documentation, AGE=85 means 85 and over, and AGE=999 means state total, while SEX=0 means total, SEX=1 means male, and SEX=2 means female:
popStateAge <- fileRead("./RInputFiles/sc-est2019-agesex-civ.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )popStateAge## # A tibble: 13,572 x 18
## SUMLEV REGION DIVISION STATE NAME SEX AGE ESTBASE2010_CIV POPEST2010_CIV
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 010 0 0 0 Unit~ 0 0 3944160 3951430
## 2 010 0 0 0 Unit~ 0 1 3978090 3957730
## 3 010 0 0 0 Unit~ 0 2 4096939 4090621
## 4 010 0 0 0 Unit~ 0 3 4119051 4111688
## 5 010 0 0 0 Unit~ 0 4 4063186 4077346
## 6 010 0 0 0 Unit~ 0 5 4056872 4064521
## 7 010 0 0 0 Unit~ 0 6 4066412 4072904
## 8 010 0 0 0 Unit~ 0 7 4030594 4042990
## 9 010 0 0 0 Unit~ 0 8 4046497 4025501
## 10 010 0 0 0 Unit~ 0 9 4148369 4125312
## # ... with 13,562 more rows, and 9 more variables: POPEST2011_CIV <dbl>,
## # POPEST2012_CIV <dbl>, POPEST2013_CIV <dbl>, POPEST2014_CIV <dbl>,
## # POPEST2015_CIV <dbl>, POPEST2016_CIV <dbl>, POPEST2017_CIV <dbl>,
## # POPEST2018_CIV <dbl>, POPEST2019_CIV <dbl># Exploration of the states and ages and sexes included
popStateAge %>% pull(NAME) %>% unique() %>% sort()## [1] "Alabama" "Alaska" "Arizona"
## [4] "Arkansas" "California" "Colorado"
## [7] "Connecticut" "Delaware" "District of Columbia"
## [10] "Florida" "Georgia" "Hawaii"
## [13] "Idaho" "Illinois" "Indiana"
## [16] "Iowa" "Kansas" "Kentucky"
## [19] "Louisiana" "Maine" "Maryland"
## [22] "Massachusetts" "Michigan" "Minnesota"
## [25] "Mississippi" "Missouri" "Montana"
## [28] "Nebraska" "Nevada" "New Hampshire"
## [31] "New Jersey" "New Mexico" "New York"
## [34] "North Carolina" "North Dakota" "Ohio"
## [37] "Oklahoma" "Oregon" "Pennsylvania"
## [40] "Rhode Island" "South Carolina" "South Dakota"
## [43] "Tennessee" "Texas" "United States"
## [46] "Utah" "Vermont" "Virginia"
## [49] "Washington" "West Virginia" "Wisconsin"
## [52] "Wyoming"popStateAge %>% pull(AGE) %>% unique() %>% sort()## [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [20] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
## [39] 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
## [58] 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
## [77] 76 77 78 79 80 81 82 83 84 85 999popStateAge %>% pull(SEX) %>% unique() %>% sort()## [1] 0 1 2The data can then be explored for the total US population estimates over time, as well as the latest population estimates as of 2019:
# Total US population estimates
popStateAge %>%
filter(AGE==999, NAME=="United States") %>%
select(state=NAME, SEX, starts_with("POPEST")) %>%
pivot_longer(-c(state, SEX)) %>%
mutate(year=as.integer(stringr::str_extract(name, "\\d{4}")),
SEX=factor(SEX, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
) %>%
ggplot(aes(x=factor(year))) +
geom_line(aes(y=value/1000000, group=SEX, color=SEX)) +
geom_text(aes(label=round(value/1000000, 1),
y=value/1000000 + ifelse(SEX=="Male", -5, 5),
color=SEX
)
) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Population (millions)", title="US Population Estimates by Year")
# Confirmation that US is the sum of the states
popStateAge %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(type=ifelse(NAME=="United States", "United States", "Component")) %>%
group_by(type, SEX, AGE, name) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(SEX, AGE, name), names_from="type", values_from="value") %>%
mutate(diff=`United States`-Component) %>%
arrange(-abs(diff))## # A tibble: 2,871 x 6
## SEX AGE name Component `United States` diff
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 0 0 ESTBASE2010_CIV 3944160 3944160 0
## 2 0 0 POPEST2010_CIV 3951430 3951430 0
## 3 0 0 POPEST2011_CIV 3963092 3963092 0
## 4 0 0 POPEST2012_CIV 3926570 3926570 0
## 5 0 0 POPEST2013_CIV 3931258 3931258 0
## 6 0 0 POPEST2014_CIV 3954787 3954787 0
## 7 0 0 POPEST2015_CIV 3983981 3983981 0
## 8 0 0 POPEST2016_CIV 3954773 3954773 0
## 9 0 0 POPEST2017_CIV 3893990 3893990 0
## 10 0 0 POPEST2018_CIV 3815343 3815343 0
## # ... with 2,861 more rows# Using POPEST2019_CIV as the key metric
popStateAge_2019 <- popStateAge %>%
filter(NAME != "United States") %>%
select(stateFull=NAME, sex=SEX, age=AGE, pop2019=POPEST2019_CIV) %>%
mutate(state=c(state.abb, "DC")[match(stateFull, c(state.name, "District of Columbia"))],
sex=factor(sex, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
)
popStateAge_2019## # A tibble: 13,311 x 5
## stateFull sex age pop2019 state
## <chr> <fct> <dbl> <dbl> <chr>
## 1 Alabama Total 0 56901 AL
## 2 Alabama Total 1 58290 AL
## 3 Alabama Total 2 59073 AL
## 4 Alabama Total 3 59799 AL
## 5 Alabama Total 4 60294 AL
## 6 Alabama Total 5 59568 AL
## 7 Alabama Total 6 58599 AL
## 8 Alabama Total 7 59537 AL
## 9 Alabama Total 8 60023 AL
## 10 Alabama Total 9 60241 AL
## # ... with 13,301 more rows# Total Population as of 2019
popStateAge_2019 %>%
filter(age==999) %>%
ggplot(aes(x=fct_reorder(state, pop2019, max))) +
geom_text(data=~filter(., sex == "Total"),
aes(label=paste0(round(pop2019/1000000, 1), " (", state, ")"), y=pop2019/1000000 + 0.1),
size=3,
hjust=0
) +
geom_col(data=~filter(., sex != "Total"), aes(y=pop2019/1000000, fill=sex), position="stack") +
coord_flip() +
labs(x=NULL, y="Population (millions)", title="2019 Population by State and Sex")
# Population by Age as of 2019
popStateAge_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(age) %>%
summarize(pop2019=sum(pop2019)) %>%
ggplot(aes(x=factor(age))) +
geom_text(aes(label=round(pop2019/1000000, 1), y=pop2019/1000000 + 0.1),
size=3,
hjust=0
) +
geom_col(aes(y=pop2019/1000000)) +
labs(x=NULL, y="Population (millions)", title="2019 Population by Age") +
coord_flip()
Population totals as well as splits by state, age, and sex seem reasonable. Age buckets are created and plotted by state:
# Add age buckets to data
popStateAgeBucket_2019 <- popStateAge_2019 %>%
mutate(bucket10=case_when(age==999 ~ "Total",
age <= 19 ~ "0-19",
age >= 80 ~ "80+",
TRUE ~ paste0(floor(age/10)*10, "-", floor(age/10)*10+9)
),
bucket03=case_when(age==999 ~ "Total",age <= 19 ~ "0-19", age >= 60 ~ "60+", TRUE ~ "20-59"),
bucketYMO=case_when(age==999 ~ "Total",age < 18 ~ "0-17", age >= 65 ~ "65+", TRUE ~ "18-64")
)
popStateAgeBucket_2019## # A tibble: 13,311 x 8
## stateFull sex age pop2019 state bucket10 bucket03 bucketYMO
## <chr> <fct> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 Alabama Total 0 56901 AL 0-19 0-19 0-17
## 2 Alabama Total 1 58290 AL 0-19 0-19 0-17
## 3 Alabama Total 2 59073 AL 0-19 0-19 0-17
## 4 Alabama Total 3 59799 AL 0-19 0-19 0-17
## 5 Alabama Total 4 60294 AL 0-19 0-19 0-17
## 6 Alabama Total 5 59568 AL 0-19 0-19 0-17
## 7 Alabama Total 6 58599 AL 0-19 0-19 0-17
## 8 Alabama Total 7 59537 AL 0-19 0-19 0-17
## 9 Alabama Total 8 60023 AL 0-19 0-19 0-17
## 10 Alabama Total 9 60241 AL 0-19 0-19 0-17
## # ... with 13,301 more rows# Proportion by bucketYMO by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state, bucketYMO) %>%
summarize(pop2019=sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucketYMO, .y=pop2019, .fun=function(x, y) -sum(y[x=="0-17"])/sum(y)))) +
geom_col(aes(y=pop2019, fill=fct_rev(bucketYMO)), position="fill") +
coord_flip() +
labs(x=NULL, y="Proportion of 2019 population", title="Age distribution by state") +
scale_fill_discrete("Age Bucket")
# Proportion by bucket10 by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state, bucket10) %>%
summarize(pop2019=sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucket10, .y=pop2019, .fun=function(x, y) -sum(y[x=="0-19"])/sum(y)))) +
geom_col(aes(y=pop2019, fill=fct_rev(bucket10)), position="fill") +
coord_flip() +
labs(x=NULL, y="Proportion of 2019 population", title="Age distribution by state") +
scale_fill_discrete("Age Bucket")
# Mean age by state
popStateAgeBucket_2019 %>%
filter(sex=="Total", age != 999) %>%
group_by(state) %>%
summarize(ageMean=sum(age*pop2019)/sum(pop2019), .groups="drop") %>%
ggplot(aes(x=fct_reorder(state, -ageMean))) +
geom_text(aes(y=ageMean+0.2, label=round(ageMean, 1)), hjust=0, size=3) +
geom_point(aes(y=ageMean)) +
coord_flip() +
labs(x=NULL, y="Average age", title="Mean age by state", subtitle="(caution that all 85+ counted as 85)") +
lims(y=c(0, NA))
There are meaningful differences in age distribution by state. Next steps are to incorporate per-capita metrics in to the hospitalization summaries:
# Integrate vpm in to the pivoted hospital data
burdenPivotList_211006$dfPivot %>%
filter(name %in% c("0-19", "20-59", "60+"), state %in% c(state.abb, "DC"), !is.na(value)) %>%
left_join(popStateAgeBucket_2019 %>%
filter(sex=="Total") %>%
group_by(state, bucket03) %>%
summarize(pop2019=sum(pop2019), .groups="drop"),
by=c("state"="state", "name"="bucket03")
) %>%
mutate(vpm=1000000*value/pop2019) %>%
ggplot(aes(x=date, y=vpm)) +
geom_line(aes(group=name, color=name)) +
labs(x=NULL, y="Newly hospitalized per million", title="Per capita newly hospitalized by age bucket") +
facet_wrap(~state, scales="free_y")
There is very significant variability by state that is not fully consistent with other metrics per capita such as cases and deaths. Further exploration is merited.
The hospitalized data in dfPivot was previously divided by total state population. The process is updated to use raw hospital data from hospAge:
ageMap <- popStateAgeBucket_2019 %>%
count(bucket10, bucket03) %>%
filter(bucket10 != "Total") %>%
select(-n)
ageMap## # A tibble: 8 x 2
## bucket10 bucket03
## <chr> <chr>
## 1 0-19 0-19
## 2 20-29 20-59
## 3 30-39 20-59
## 4 40-49 20-59
## 5 50-59 20-59
## 6 60-69 60+
## 7 70-79 60+
## 8 80+ 60+# Create hospitalized by age bucket by state data
hospBucketState <- burdenPivotList_211006$hospAge %>%
left_join(ageMap, by=c("age"="bucket10")) %>%
filter(!is.na(value)) %>%
group_by(state, date, bucket03) %>%
summarize(value=sum(value), .groups="drop") %>%
filter(state %in% c(state.abb, "DC")) %>%
left_join(popStateAgeBucket_2019 %>%
filter(sex=="Total") %>%
group_by(state, bucket03) %>%
summarize(pop2019=sum(pop2019), .groups="drop"),
by=c("state", "bucket03")
) %>%
mutate(vpm=1000000*value/pop2019) %>%
group_by(state, bucket03) %>%
arrange(date) %>%
mutate(vpm7=zoo::rollmean(vpm, k=7, fill=NA), vpmcum=cumsum(vpm)) %>%
ungroup()
hospBucketState %>%
ggplot(aes(x=date, y=vpm7)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y="Newly hospitalized per million (rolling 7-day mean)",
title="Per million newly hospitalized by age bucket"
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")## Warning: Removed 18 row(s) containing missing values (geom_path).
hospBucketState %>%
filter(state != "ND", date >= "2020-07-15") %>%
ggplot(aes(x=date, y=vpmcum/1000)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y="Cumulative hospitalized per thousand since 2020-07-15",
title="Cumulative newly hospitalized per thousand by age bucket",
subtitle="Since 2020-07-15, excludes ND"
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")
The process to create population data by state and age is converted to functional form:
readPopStateAge <- function(loc) {
# FUNCTION ARGUMENTS:
# loc: file location on the local computer
# Read the data
df <- fileRead(loc) %>%
checkUniqueRows(uniqueBy=c("NAME", "SEX", "AGE"))
# Confirm that states, ages, and sexes are as expected
a1 <- all.equal(sort(c(state.name, "District of Columbia", "United States")),
df %>% pull(NAME) %>% unique() %>% sort()
)
print(a1)
a2 <- all.equal(c(0:85, 999), df %>% pull(AGE) %>% unique() %>% sort())
print(a2)
a3 <- all.equal(0:2, df %>% pull(SEX) %>% unique() %>% sort())
print(a3)
if(!isTRUE(a1) | !isTRUE(a2) | !isTRUE(a3)) stop("\nUnexpected values for state, age, or sex\n")
# Plot for total US population estimates
p1 <- df %>%
filter(AGE==999, NAME=="United States") %>%
select(state=NAME, SEX, starts_with("POPEST")) %>%
pivot_longer(-c(state, SEX)) %>%
mutate(year=as.integer(stringr::str_extract(name, "\\d{4}")),
SEX=factor(SEX, levels=c("0", "1", "2"), labels=c("Total", "Male", "Female"))
) %>%
ggplot(aes(x=factor(year))) +
geom_line(aes(y=value/1000000, group=SEX, color=SEX)) +
geom_text(aes(label=round(value/1000000, 1),
y=value/1000000 + ifelse(SEX=="Male", -5, 5),
color=SEX
)
) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Population (millions)", title="US Population Estimates by Year")
print(p1)
componentCheck <- function(vrbl, sumName, descMessage) {
otherVars <- setdiff(c("NAME", "SEX", "AGE"), vrbl)
dfCheck <- df %>%
select(-c(SUMLEV, REGION, DIVISION, STATE)) %>%
pivot_longer(-c(NAME, SEX, AGE)) %>%
mutate(across(.cols=all_of(vrbl), .fns=~ifelse(.x==sumName, "Total", "Component"), .names="type")) %>%
group_by_at(c("type", all_of(otherVars), "name")) %>%
summarize(value=sum(value), .groups="drop") %>%
pivot_wider(c(all_of(otherVars), "name"), names_from="type", values_from="value") %>%
mutate(diff=Total-Component) %>%
arrange(-abs(diff))
if(max(abs(dfCheck$diff)) > 0) {
print(dfCheck)
stop(paste0("\nFAILED CHECK: ", descMessage, "\n"))
} else {
cat("\nPASSED CHECK:", descMessage, "\n\n")
}
}
componentCheck("NAME", sumName="United States", descMessage="United States total is the sum of states and DC")
componentCheck("AGE", sumName=999, descMessage="Age 999 total is the sum of the ages")
componentCheck("SEX", sumName=0, descMessage="Sex 0 total is the sum of the sexes")
# Return the data
df
}
filterPopStateAge <- function(df, keyCol, keyColName=keyCol, yearLabel=NULL) {
# FUNCTION ARGUENTS:
# df: loaded data frame with columns
# keyCol: the population column to select
# keyColName: renaming to be applied for the population column (default will leave as-is)
# yearLabel: label for year to use in plots (NULL means infer from keyCol)
if (is.null(yearLabel)) yearLabel <- stringr::str_extract(keyCol, pattern="\\d{4}")
# Create the selection and renaming vector
useCols <- c("stateFull", "sex", "age", all_of(keyColName))
names(useCols) <- c("NAME", "SEX", "AGE", all_of(keyCol))
# Select the key variable and rename, add state abbreviation, convert sex to more interpretable factor
dfFilter <- df %>%
colSelector(names(useCols)) %>%
colRenamer(useCols) %>%
mutate(state=c(state.abb, "DC", "US")[match(stateFull,
c(state.name, "District of Columbia", "United States")
)
],
sex=factor(sex, levels=c(0, 1, 2), labels=c("Total", "Male", "Female"))
)
# Plot for total population in the key year
p1 <- dfFilter %>%
filter(age==999, state != "US") %>%
ggplot(aes(x=fct_reorder(state, get(keyColName), max))) +
geom_text(data=~filter(., sex == "Total"),
aes(label=paste0(round(get(keyColName)/1000000, 1), " (", state, ")"),
y=get(keyColName)/1000000 + 0.1
),
size=3,
hjust=0
) +
geom_col(data=~filter(., sex != "Total"), aes(y=get(keyColName)/1000000, fill=sex), position="stack") +
coord_flip() +
labs(x=NULL, y="Population (millions)", title=paste0(yearLabel, " Population by State and Sex"))
print(p1)
# Population by Age in the key year
p2 <- dfFilter %>%
filter(sex=="Total", age != 999, state != "US") %>%
group_by(age) %>%
summarize(across(.cols=all_of(keyColName), sum)) %>%
ggplot(aes(x=factor(age))) +
geom_text(aes(label=round(get(keyColName)/1000000, 1), y=get(keyColName)/1000000 + 0.1),
size=3,
hjust=0
) +
geom_col(aes(y=get(keyColName)/1000000), fill="lightblue") +
labs(x=NULL, y="Population (millions)", title=paste0(yearLabel, " Population by Age")) +
coord_flip()
print(p2)
# Return the data
dfFilter
}
bucketPopStateAge <- function(df, popVar, popYearLabel=NULL) {
# FUNCTION ARGUMENTS:
# df: a filtered data frame containing the year of interest
# popVar: name of the population variable
# popYearLabel: year to use in the plot titles (NULL means infer from popVar)
# Infer popYearLabel if not provided
if(is.null(popYearLabel)) popYearLabel <- stringr::str_extract(popVar, pattern="\\d{4}")
# Add age buckets to data
dfBucket <- df %>%
mutate(bucket10=case_when(age==999 ~ "Total",
age <= 19 ~ "0-19",
age >= 80 ~ "80+",
TRUE ~ paste0(floor(age/10)*10, "-", floor(age/10)*10+9)
),
bucket03=case_when(age==999 ~ "Total",age <= 19 ~ "0-19", age >= 60 ~ "60+", TRUE ~ "20-59"),
bucketYMO=case_when(age==999 ~ "Total",age < 18 ~ "0-17", age >= 65 ~ "65+", TRUE ~ "18-64")
)
# Check that buckets worked as intended
checkBucket <- function(keyVar) {
dfBucket %>%
count(age, y=get(keyVar)) %>%
ggplot(aes(x=factor(age), y=y)) +
geom_tile(aes(fill=n)) +
coord_flip() +
labs(x=NULL, y=NULL, title=paste0("Age map for: ", keyVar))
}
p1 <- checkBucket(keyVar="bucket10")
p2 <- checkBucket(keyVar="bucket03")
p3 <- checkBucket(keyVar="bucketYMO")
gridExtra::grid.arrange(p1, p2, p3, nrow=1)
# Proportion by bucketYMO by state
p4 <- dfBucket %>%
filter(sex=="Total", age != 999, state != "US") %>%
group_by(state, bucketYMO) %>%
summarize(pop=sum(get(popVar)), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucketYMO, .y=pop, .fun=function(x, y) -sum(y[x=="0-17"])/sum(y)))) +
geom_col(aes(y=pop, fill=fct_rev(bucketYMO)), position="fill") +
coord_flip() +
labs(x=NULL,
y=paste0("Proportion of ", popYearLabel, " population"),
title="Age distribution by state"
) +
scale_fill_discrete("Age Bucket")
print(p4)
# Proportion by bucket10 by state
p5 <- dfBucket %>%
filter(sex=="Total", age != 999, state != "US") %>%
group_by(state, bucket10) %>%
summarize(pop=sum(get(popVar)), .groups="drop") %>%
ggplot(aes(x=fct_reorder2(state, .x=bucket10, .y=pop, .fun=function(x, y) -sum(y[x=="0-19"])/sum(y)))) +
geom_col(aes(y=pop, fill=fct_rev(bucket10)), position="fill") +
coord_flip() +
labs(x=NULL,
y=paste0("Proportion of ", popYearLabel, " population"),
title="Age distribution by state"
) +
scale_fill_discrete("Age Bucket")
print(p5)
# Mean age by state
p6 <- dfBucket %>%
filter(sex=="Total", age != 999, state != "US") %>%
group_by(state) %>%
mutate(age=ifelse(age==85, 90, age)) %>%
summarize(ageMean=sum(age*get(popVar))/sum(get(popVar)), .groups="drop") %>%
ggplot(aes(x=fct_reorder(state, -ageMean))) +
geom_text(aes(y=ageMean+0.2, label=round(ageMean, 1)), hjust=0, size=3) +
geom_point(aes(y=ageMean)) +
coord_flip() +
labs(x=NULL,
y="Average age",
title="Mean age by state",
subtitle="(caution that all 85+ counted as 90 for mean calculation)"
) +
lims(y=c(0, NA))
print(p6)
# Return the bucketed data
dfBucket
}
dfStateAgeBucket <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
bucketPopStateAge(popVar="pop2019")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )##
## *** File has been checked for uniqueness by: NAME SEX AGE
##
## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## PASSED CHECK: United States total is the sum of states and DC
##
##
## PASSED CHECK: Age 999 total is the sum of the ages
##
##
## PASSED CHECK: Sex 0 total is the sum of the sexes
dfStateAgeBucket## # A tibble: 13,572 x 8
## stateFull sex age pop2019 state bucket10 bucket03 bucketYMO
## <chr> <fct> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 United States Total 0 3783052 US 0-19 0-19 0-17
## 2 United States Total 1 3829599 US 0-19 0-19 0-17
## 3 United States Total 2 3922044 US 0-19 0-19 0-17
## 4 United States Total 3 3998665 US 0-19 0-19 0-17
## 5 United States Total 4 4043323 US 0-19 0-19 0-17
## 6 United States Total 5 4028281 US 0-19 0-19 0-17
## 7 United States Total 6 4017227 US 0-19 0-19 0-17
## 8 United States Total 7 4022319 US 0-19 0-19 0-17
## 9 United States Total 8 4066194 US 0-19 0-19 0-17
## 10 United States Total 9 4061874 US 0-19 0-19 0-17
## # ... with 13,562 more rowsAge-adjusted plots of hospitaliztion are also created:
hospAgePerCapita <- function(dfBucket,
lst,
popVar,
excludeState=c(),
cumStartDate=NULL
) {
# FUNCTION ARGUMENTS:
# dfBucket: data frame containing bucketed age data by state
# lst: a processed list file containing $hospAge
# popVar: name of the population variable in dfBucket
# excludeState: list of states to exclude from cumulative plot
# cumStateDate: data to start the cumulative plots (NULL means use earliest date in data)
# Find cumStartDate if not passed
if(is.null(cumStartDate)) cumStartDate <- lst[["hospAge"]]$date %>% min()
# Create mapping from bucket10 to bucket03
ageMap10to03 <- dfBucket %>%
count(bucket10, bucket03) %>%
select(-n)
# Create population by state and bucket03
popStateBucket03 <- dfBucket %>%
filter(sex=="Total", state != "US") %>%
group_by(state, bucket03) %>%
summarize(pop=sum(get(popVar)), .groups="drop")
# Create hospitalized by age bucket by state data
dfUse <- lst[["hospAge"]] %>%
left_join(ageMap10to03, by=c("age"="bucket10")) %>%
filter(!is.na(value)) %>%
group_by(state, date, bucket03) %>%
summarize(value=sum(value), .groups="drop") %>%
filter(state %in% c(state.abb, "DC")) %>%
left_join(popStateBucket03, by=c("state", "bucket03")) %>%
mutate(vpm=1000000*value/pop) %>%
group_by(state, bucket03) %>%
arrange(date) %>%
mutate(vpm7=zoo::rollmean(vpm, k=7, fill=NA), vpmcum=cumsum(vpm)) %>%
ungroup()
p1 <- dfUse %>%
ggplot(aes(x=date, y=vpm7)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y="Newly hospitalized per million (rolling 7-day mean)",
title="Per million newly hospitalized by age bucket"
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")
print(p1)
p2 <- dfUse %>%
filter(!(state %in% all_of(excludeState)), date >= cumStartDate) %>%
ggplot(aes(x=date, y=vpmcum/1000)) +
geom_line(aes(group=bucket03, color=bucket03)) +
labs(x=NULL,
y=paste0("Cumulative hospitalized per thousand since ", cumStartDate),
title="Cumulative newly hospitalized per thousand by age bucket",
subtitle=paste0("Since ",
cumStartDate,
if(length(excludeState) > 0) paste0(", excludes ", paste0(excludeState, collapse=", "))
else ""
)
) +
facet_wrap(~state, scales="free_y") +
scale_color_discrete("Age")
print(p2)
# Return the dataset
dfUse
}
hospAgePerCapita(dfStateAgeBucket,
lst=burdenPivotList_211006,
popVar="pop2019",
excludeState=c("ND"),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
## # A tibble: 68,684 x 8
## state date bucket03 value pop vpm vpm7 vpmcum
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NV 2020-01-02 0-19 7 760090 9.21 NA 9.21
## 2 NV 2020-01-02 20-59 80 1627729 49.1 NA 49.1
## 3 NV 2020-01-02 60+ 156 681510 229. NA 229.
## 4 NV 2020-01-03 0-19 7 760090 9.21 NA 18.4
## 5 NV 2020-01-03 20-59 80 1627729 49.1 NA 98.3
## 6 NV 2020-01-03 60+ 156 681510 229. NA 458.
## 7 AR 2020-01-04 0-19 0 778802 0 NA 0
## 8 AR 2020-01-04 20-59 0 1521886 0 NA 0
## 9 AR 2020-01-04 60+ 0 711854 0 NA 0
## 10 NV 2020-01-04 0-19 7 760090 9.21 NA 27.6
## # ... with 68,674 more rowsThe full post-process is converted to functional form, with the option to return key elements in the list:
# Can be run only as-needed
dfStateAgeBucket <- readPopStateAge("./RInputFiles/sc-est2019-agesex-civ.csv") %>%
filterPopStateAge(keyCol="POPEST2019_CIV", keyColName="pop2019") %>%
bucketPopStateAge(popVar="pop2019")##
## -- Column specification --------------------------------------------------------
## cols(
## SUMLEV = col_character(),
## REGION = col_double(),
## DIVISION = col_double(),
## STATE = col_double(),
## NAME = col_character(),
## SEX = col_double(),
## AGE = col_double(),
## ESTBASE2010_CIV = col_double(),
## POPEST2010_CIV = col_double(),
## POPEST2011_CIV = col_double(),
## POPEST2012_CIV = col_double(),
## POPEST2013_CIV = col_double(),
## POPEST2014_CIV = col_double(),
## POPEST2015_CIV = col_double(),
## POPEST2016_CIV = col_double(),
## POPEST2017_CIV = col_double(),
## POPEST2018_CIV = col_double(),
## POPEST2019_CIV = col_double()
## )##
## *** File has been checked for uniqueness by: NAME SEX AGE
##
## [1] TRUE
## [1] TRUE
## [1] TRUE
##
## PASSED CHECK: United States total is the sum of states and DC
##
##
## PASSED CHECK: Age 999 total is the sum of the ages
##
##
## PASSED CHECK: Sex 0 total is the sum of the sexes
# Create pivoted burden data
burdenPivotList_211006 <- postProcessCDCDaily(cdc_daily_211006,
dataThruLabel="Sep 2021",
keyStates=state.abb[state.region=="South"],
keyDatesBurden=c("2021-09-30", "2021-03-30",
"2020-09-30", "2020-03-31"
),
keyDatesVaccine=c("2021-09-30", "2021-07-31",
"2021-05-31", "2021-03-31"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
# Create hospitalized per capita data
hospAgePerCapita(dfStateAgeBucket,
lst=burdenPivotList_211006,
popVar="pop2019",
excludeState=c("ND"),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
## # A tibble: 68,684 x 8
## state date bucket03 value pop vpm vpm7 vpmcum
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NV 2020-01-02 0-19 7 760090 9.21 NA 9.21
## 2 NV 2020-01-02 20-59 80 1627729 49.1 NA 49.1
## 3 NV 2020-01-02 60+ 156 681510 229. NA 229.
## 4 NV 2020-01-03 0-19 7 760090 9.21 NA 18.4
## 5 NV 2020-01-03 20-59 80 1627729 49.1 NA 98.3
## 6 NV 2020-01-03 60+ 156 681510 229. NA 458.
## 7 AR 2020-01-04 0-19 0 778802 0 NA 0
## 8 AR 2020-01-04 20-59 0 1521886 0 NA 0
## 9 AR 2020-01-04 60+ 0 711854 0 NA 0
## 10 NV 2020-01-04 0-19 7 760090 9.21 NA 27.6
## # ... with 68,674 more rowsThe full process is run with the latest data and cached:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211024.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211024.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211024.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_211006")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_211006")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_211006")$dfRaw$vax
)
cdc_daily_211024 <- readRunCDCDaily(thruLabel="Oct 23, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 18
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-03 tot_deaths 269 244 25 0.09746589
## 2 2020-02-04 tot_deaths 269 244 25 0.09746589
## 3 2020-02-05 tot_deaths 269 244 25 0.09746589
## 4 2020-02-06 tot_deaths 269 244 25 0.09746589
## 5 2020-02-07 tot_deaths 269 244 25 0.09746589
## 6 2020-02-08 tot_deaths 270 245 25 0.09708738
## 7 2020-02-09 tot_deaths 270 245 25 0.09708738
## 8 2020-02-10 tot_deaths 270 245 25 0.09708738
## 9 2020-02-11 tot_deaths 270 245 25 0.09708738
## 10 2020-02-12 tot_deaths 270 245 25 0.09708738
## 11 2020-02-13 tot_deaths 270 245 25 0.09708738
## 12 2020-02-14 tot_deaths 270 245 25 0.09708738
## 13 2020-02-15 tot_deaths 270 245 25 0.09708738
## 14 2020-02-16 tot_deaths 270 245 25 0.09708738
## 15 2020-02-17 tot_deaths 270 245 25 0.09708738
## 16 2020-02-18 tot_deaths 270 245 25 0.09708738
## 17 2020-02-19 tot_deaths 271 246 25 0.09671180
## 18 2020-02-20 tot_deaths 271 246 25 0.09671180
## 19 2020-02-21 tot_deaths 271 246 25 0.09671180
## 20 2020-02-22 tot_deaths 271 246 25 0.09671180
## 21 2020-02-23 tot_deaths 271 246 25 0.09671180
## 22 2020-02-24 tot_deaths 271 246 25 0.09671180
## 23 2020-02-25 tot_deaths 271 246 25 0.09671180
## 24 2020-02-26 tot_deaths 271 246 25 0.09671180
## 25 2020-02-27 tot_deaths 272 247 25 0.09633911
## 26 2020-02-28 tot_deaths 272 247 25 0.09633911
## 27 2020-02-29 tot_deaths 273 248 25 0.09596929
## 28 2020-03-01 tot_deaths 273 248 25 0.09596929
## 29 2020-03-02 tot_deaths 279 254 25 0.09380863
## 30 2020-03-03 tot_deaths 282 257 25 0.09276438
## 31 2020-03-04 tot_deaths 284 259 25 0.09208103
## 32 2020-03-05 tot_deaths 287 262 25 0.09107468
## 33 2020-03-06 tot_deaths 291 266 25 0.08976661
## 34 2020-03-07 tot_deaths 296 271 25 0.08818342
## 35 2020-03-08 tot_deaths 301 276 25 0.08665511
## 36 2020-03-09 tot_deaths 305 280 25 0.08547009
## 37 2020-03-10 tot_deaths 311 286 25 0.08375209
## 38 2020-03-11 tot_deaths 325 300 25 0.08000000
## 39 2020-03-12 tot_deaths 332 307 25 0.07824726
## 40 2020-03-13 tot_deaths 343 318 25 0.07564297
## 41 2020-03-14 tot_deaths 357 332 25 0.07256894
## 42 2020-03-15 tot_deaths 377 352 25 0.06858711
## 43 2020-03-16 tot_deaths 398 373 25 0.06485084
## 44 2020-03-17 tot_deaths 430 405 25 0.05988024
## 45 2020-03-18 tot_deaths 501 476 25 0.05117707
## 46 2021-10-03 new_deaths 910 687 223 0.27927364
## 47 2021-09-26 new_deaths 1068 817 251 0.26631300
## 48 2021-09-25 new_deaths 1385 1116 269 0.21511395
## 49 2021-10-02 new_deaths 1164 997 167 0.15455807
## 50 2021-09-19 new_deaths 1202 1036 166 0.14834674
## 51 2021-09-27 new_deaths 1307 1127 180 0.14790468
## 52 2021-09-21 new_deaths 2208 1930 278 0.13436443
## 53 2021-09-24 new_deaths 2198 1925 273 0.13242784
## 54 2021-09-28 new_deaths 1987 1754 233 0.12456562
## 55 2021-09-23 new_deaths 1943 1730 213 0.11598149
## 56 2021-09-22 new_deaths 2016 1802 214 0.11210058
## 57 2021-09-18 new_deaths 1500 1358 142 0.09937019
## 58 2021-09-12 new_deaths 1283 1192 91 0.07353535
## 59 2021-09-20 new_deaths 1334 1247 87 0.06741573
## 60 2021-09-13 new_deaths 1364 1276 88 0.06666667
## 61 2021-09-17 new_deaths 2102 1973 129 0.06331288
## 62 2021-09-16 new_deaths 1947 1832 115 0.06086266
## 63 2021-09-11 new_deaths 1572 1481 91 0.05961349
## 64 2021-08-01 new_deaths 408 386 22 0.05541562
## 65 2021-09-15 new_deaths 2109 1996 113 0.05505481
## 66 2021-09-05 new_deaths 1245 1184 61 0.05022643
## 67 2021-10-03 new_cases 74729 70013 4716 0.06516422
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 AL tot_deaths 3924524 3909338 15186 0.003877015
## 2 KY tot_deaths 2373233 2364995 8238 0.003477249
## 3 FL tot_deaths 13520925 13485213 35712 0.002644732
## 4 CA tot_deaths 20286061 20243442 42619 0.002103110
## 5 TN tot_deaths 3992595 3988072 4523 0.001133489
## 6 NM tot_deaths 1421364 1419795 1569 0.001104479
## 7 FL new_deaths 57919 55622 2297 0.040461155
## 8 KY new_deaths 9155 8906 249 0.027573224
## 9 AL new_deaths 14858 14542 316 0.021496599
## 10 TN new_deaths 15592 15323 269 0.017402555
## 11 CA new_deaths 69969 68795 1174 0.016920815
## 12 IN new_deaths 15986 15773 213 0.013413521
## 13 NM new_deaths 4869 4823 46 0.009492365
## 14 SC new_deaths 12933 12828 105 0.008151857
## 15 MS new_deaths 8856 8906 50 0.005629997
## 16 PR new_deaths 3181 3173 8 0.002518099
## 17 RI new_deaths 2846 2843 3 0.001054667
## 18 KY new_cases 705033 700393 4640 0.006602980
## 19 CA new_cases 4743900 4724440 19460 0.004110541
## 20 TN new_cases 1242274 1238023 4251 0.003427815
## 21 SC new_cases 868617 866245 2372 0.002734511
## 22 AL new_cases 805018 802977 2041 0.002538565
## 23 PR new_cases 181993 181797 196 0.001077545
##
##
##
## Raw file for cdcDaily:
## Rows: 38,400
## Columns: 15
## $ date <date> 2021-09-01, 2021-01-13, 2020-04-17, 2020-07-30, 2021-0~
## $ state <chr> "ND", "IN", "VI", "ME", "MS", "NH", "NV", "NE", "NC", "~
## $ tot_cases <dbl> 118491, 574488, 54, 3910, 280182, 2518, 320719, 20150, ~
## $ conf_cases <dbl> 107475, NA, NA, 3497, 176228, NA, 320719, NA, 760095, N~
## $ prob_cases <dbl> 11016, NA, NA, 413, 103954, NA, 0, NA, 115264, NA, 2026~
## $ new_cases <dbl> 536, 3654, 1, 22, 1059, 89, 180, 179, 1614, 0, 621, 275~
## $ pnew_case <dbl> 66, 0, NA, 2, 559, 0, 0, 0, 450, NA, -11, 0, 0, NA, 163~
## $ tot_deaths <dbl> 1562, 10920, 2, 123, 6730, 86, 5530, 282, 12363, 0, 328~
## $ conf_death <dbl> NA, 10553, NA, 122, 4739, NA, NA, NA, 10933, NA, 2524, ~
## $ prob_death <dbl> NA, 367, NA, 1, 1991, NA, NA, NA, 1430, NA, 761, 250, N~
## $ new_deaths <dbl> 1, 74, 1, 2, 13, 2, 0, -1, 16, 0, 66, 40, 33, 0, 16, 15~
## $ pnew_death <dbl> 0, 1, NA, 0, 7, 0, 0, 0, 2, NA, 8, 0, 0, NA, 3, 0, 0, 2~
## $ created_at <chr> "09/02/2021 01:49:05 PM", "01/13/2021 12:00:00 AM", "04~
## $ consent_cases <chr> "Agree", "Not agree", NA, "Agree", "Agree", "Not agree"~
## $ consent_deaths <chr> "Not agree", "Agree", NA, "Agree", "Agree", "Not agree"~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 19
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 4135 4781 646 0.14490803
## 2 2020-07-25 hosp_ped 4082 4407 325 0.07656968
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 UT inp 168050 167794 256 0.001524517
## 2 NH hosp_ped 336 380 44 0.122905028
## 3 ME hosp_ped 664 722 58 0.083694084
## 4 WV hosp_ped 2824 3069 245 0.083149499
## 5 VT hosp_ped 113 109 4 0.036036036
## 6 SC hosp_ped 4747 4918 171 0.035385411
## 7 KS hosp_ped 2473 2528 55 0.021995601
## 8 DE hosp_ped 2475 2436 39 0.015882712
## 9 AR hosp_ped 7856 7774 82 0.010492642
## 10 NJ hosp_ped 10600 10704 104 0.009763425
## 11 TN hosp_ped 11961 11849 112 0.009407812
## 12 AZ hosp_ped 15019 15159 140 0.009278282
## 13 IN hosp_ped 9881 9966 85 0.008565526
## 14 MA hosp_ped 5780 5731 49 0.008513596
## 15 UT hosp_ped 3907 3876 31 0.007966080
## 16 MO hosp_ped 21184 21343 159 0.007477602
## 17 NM hosp_ped 3935 3906 29 0.007397016
## 18 VA hosp_ped 9061 9114 53 0.005832187
## 19 MS hosp_ped 6363 6333 30 0.004725898
## 20 AK hosp_ped 1059 1064 5 0.004710316
## 21 MD hosp_ped 6786 6815 29 0.004264392
## 22 GA hosp_ped 30214 30330 116 0.003831924
## 23 CO hosp_ped 11892 11848 44 0.003706824
## 24 RI hosp_ped 1705 1699 6 0.003525264
## 25 IA hosp_ped 3553 3541 12 0.003383141
## 26 FL hosp_ped 68359 68589 230 0.003358939
## 27 WA hosp_ped 6319 6300 19 0.003011332
## 28 OK hosp_ped 15596 15557 39 0.002503772
## 29 ND hosp_ped 1779 1783 4 0.002245929
## 30 AL hosp_ped 12216 12189 27 0.002212661
## 31 WY hosp_ped 467 468 1 0.002139037
## 32 PR hosp_ped 13592 13618 26 0.001911062
## 33 TX hosp_ped 61576 61459 117 0.001901898
## 34 NV hosp_ped 2739 2744 5 0.001823819
## 35 NE hosp_ped 4153 4146 7 0.001686950
## 36 PA hosp_ped 25406 25444 38 0.001494592
## 37 MN hosp_ped 7296 7305 9 0.001232792
## 38 WV hosp_adult 176432 176179 253 0.001435009
## 39 NH hosp_adult 47027 46970 57 0.001212805
## 40 NV hosp_adult 418591 418168 423 0.001011044
##
##
##
## Raw file for cdcHosp:
## Rows: 32,249
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current: Administered_Fed_LTC Administered_Fed_LTC_Residents Administered_Fed_LTC_Staff Administered_Fed_LTC_Unk Administered_Fed_LTC_Dose1 Administered_Fed_LTC_Dose1_Residents Administered_Fed_LTC_Dose1_Staff Administered_Fed_LTC_Dose1_Unk Series_Complete_FedLTC Series_Complete_FedLTC_Residents Series_Complete_FedLTC_Staff Series_Complete_FedLTC_Unknown
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 19
##
## Checking for similarity of: state
## In reference but not in current: LTC
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-02-05 vxa 73638424 78058057 4419633 0.05826942
## 2 2021-01-22 vxa 38215918 40505202 2289284 0.05816188
## 3 2021-02-04 vxa 70407420 74617447 4210027 0.05805938
## 4 2021-01-25 vxa 45468486 48182973 2714487 0.05796999
## 5 2021-01-21 vxa 35092748 37181929 2089181 0.05781225
## 6 2021-01-29 vxa 55769322 59083588 3314266 0.05771323
## 7 2021-02-03 vxa 67756508 71778915 4022407 0.05765428
## 8 2021-01-23 vxa 41075980 43513650 2437670 0.05763520
## 9 2021-02-06 vxa 78075928 82704890 4628962 0.05758102
## 10 2021-01-30 vxa 59155804 62661235 3505431 0.05755239
## 11 2021-01-28 vxa 52387364 55489498 3102134 0.05751250
## 12 2021-01-31 vxa 62246598 65930316 3683718 0.05747865
## 13 2021-01-27 vxa 49305268 52210108 2904840 0.05722956
## 14 2021-02-02 vxa 65561720 69413180 3851460 0.05706928
## 15 2021-01-24 vxa 43697310 46264328 2567018 0.05706917
## 16 2021-02-07 vxa 82421874 87261018 4839144 0.05703750
## 17 2021-02-08 vxa 84835234 89781717 4946483 0.05665524
## 18 2021-02-01 vxa 64444804 68193583 3748779 0.05652631
## 19 2021-02-09 vxa 86412380 91427604 5015224 0.05640153
## 20 2021-01-26 vxa 47081988 49807818 2725830 0.05626660
## 21 2021-02-10 vxa 89539940 94716439 5176499 0.05618800
## 22 2021-01-20 vxa 33050538 34958794 1908256 0.05611748
## 23 2021-02-11 vxa 92780540 98129315 5348775 0.05603456
## 24 2021-01-19 vxa 32167926 34010489 1842563 0.05568471
## 25 2021-01-16 vxa 28117300 29717126 1599826 0.05532435
## 26 2021-02-12 vxa 96821116 102328901 5507785 0.05531293
## 27 2021-01-17 vxa 29829518 31522857 1693339 0.05520044
## 28 2021-01-15 vxa 25835214 27291672 1456458 0.05482941
## 29 2021-01-18 vxa 30925618 32660620 1735002 0.05457162
## 30 2021-02-13 vxa 101283768 106950859 5667091 0.05442986
## 31 2021-02-14 vxa 105768712 111591583 5822871 0.05357806
## 32 2021-01-14 vxa 23922748 25234457 1311709 0.05336792
## 33 2021-02-15 vxa 108521140 114435230 5914090 0.05305155
## 34 2021-02-16 vxa 110440728 116409270 5968542 0.05262105
## 35 2021-02-17 vxa 112563654 118633995 6070341 0.05251213
## 36 2021-01-13 vxa 21735014 22899854 1164840 0.05219417
## 37 2021-02-18 vxa 115475534 121657530 6181996 0.05213947
## 38 2021-01-12 vxa 19760044 20792808 1032764 0.05093422
## 39 2021-02-19 vxa 122008442 128297630 6289188 0.05025198
## 40 2021-02-20 vxa 125486643 131885653 6399010 0.04972571
## 41 2021-02-21 vxa 129171433 135691281 6519848 0.04923191
## 42 2021-02-22 vxa 131411783 137991019 6579236 0.04884311
## 43 2021-01-11 vxa 18726328 19663344 937016 0.04881605
## 44 2021-01-10 vxa 17887448 18780252 892804 0.04869703
## 45 2021-02-23 vxa 133182811 139810631 6627820 0.04855662
## 46 2021-02-24 vxa 136109901 142834211 6724310 0.04821260
## 47 2021-01-09 vxa 16433258 17243018 809760 0.04809083
## 48 2021-02-25 vxa 139808950 146626410 6817460 0.04760208
## 49 2021-02-26 vxa 144257510 151190581 6933071 0.04693258
## 50 2021-02-27 vxa 149124582 156168122 7043540 0.04614286
## 51 2021-01-08 vxa 14852290 15545536 693246 0.04561155
## 52 2021-02-28 vxa 154092179 161204716 7112537 0.04511644
## 53 2021-01-07 vxa 13111360 13714673 603313 0.04497967
## 54 2021-03-01 vxa 157489989 164644520 7154531 0.04441953
## 55 2021-03-02 vxa 160979444 168158060 7178616 0.04362077
## 56 2021-01-06 vxa 11649960 12161595 511635 0.04297367
## 57 2021-03-03 vxa 164872447 172091102 7218655 0.04284532
## 58 2021-03-04 vxa 169028852 176290956 7262104 0.04206016
## 59 2021-03-05 vxa 173999326 181305751 7306425 0.04112761
## 60 2021-01-05 vxa 10449850 10878916 429066 0.04023355
## 61 2021-03-06 vxa 179915926 187265421 7349495 0.04003196
## 62 2021-03-07 vxa 184896239 192285341 7389102 0.03918061
## 63 2021-03-08 vxa 188442277 195848093 7405816 0.03854281
## 64 2021-03-09 vxa 191740870 199160110 7419240 0.03795969
## 65 2021-03-10 vxa 195890209 203336007 7445798 0.03730115
## 66 2021-01-04 vxa 9775622 10140739 365117 0.03666503
## 67 2021-03-11 vxa 200963911 208437508 7473597 0.03650987
## 68 2021-03-12 vxa 206928026 214427848 7499822 0.03559852
## 69 2021-01-03 vxa 9296678 9627378 330700 0.03495022
## 70 2021-03-13 vxa 216273799 223812784 7538985 0.03426137
## 71 2021-03-14 vxa 219000382 226547859 7547477 0.03387951
## 72 2021-03-15 vxa 223169272 230728466 7559194 0.03330792
## 73 2021-03-16 vxa 226510722 234079842 7569120 0.03286702
## 74 2021-03-17 vxa 231194582 238780518 7585936 0.03228229
## 75 2021-03-18 vxa 236683177 244297330 7614153 0.03166096
## 76 2021-01-01 vxa 8345316 8611185 265869 0.03135895
## 77 2021-01-02 vxa 8926120 9208860 282740 0.03118173
## 78 2020-12-31 vxa 7476260 7712757 236497 0.03114053
## 79 2021-03-19 vxa 241951483 249582189 7630706 0.03104856
## 80 2021-03-20 vxa 248304380 255952591 7648211 0.03033458
## 81 2021-03-21 vxa 254494279 262157192 7662913 0.02966376
## 82 2021-03-22 vxa 258617091 266288288 7671197 0.02922888
## 83 2021-03-23 vxa 262046215 269722005 7675790 0.02886893
## 84 2021-03-24 vxa 266630324 274314227 7683903 0.02840921
## 85 2021-03-25 vxa 272372791 280063437 7690646 0.02784266
## 86 2021-03-26 vxa 279261974 286961402 7699428 0.02719573
## 87 2021-03-27 vxa 286363038 294070895 7707857 0.02655895
## 88 2020-12-30 vxa 6329704 6496853 167149 0.02606296
## 89 2021-03-28 vxa 293046372 300762295 7715923 0.02598791
## 90 2021-03-29 vxa 297812331 305530367 7718036 0.02558425
## 91 2021-03-30 vxa 301401971 309121935 7719964 0.02528964
## 92 2021-03-31 vxa 306831236 314557736 7726500 0.02486848
## 93 2021-04-01 vxa 313649661 321381014 7731353 0.02434954
## 94 2021-04-02 vxa 321693564 329428906 7735342 0.02376002
## 95 2021-04-03 vxa 329958345 337697137 7738792 0.02318199
## 96 2021-04-04 vxa 336791939 344534065 7742126 0.02272664
## 97 2021-04-05 vxa 341092155 348835528 7743373 0.02244691
## 98 2021-04-06 vxa 343928772 351677392 7748620 0.02227876
## 99 2021-04-07 vxa 349771863 357522867 7751004 0.02191732
## 100 2021-04-08 vxa 356650336 364404792 7754456 0.02150864
## 101 2021-04-09 vxa 364645110 372404496 7759386 0.02105526
## 102 2021-04-10 vxa 374016582 381780588 7764006 0.02054521
## 103 2021-04-11 vxa 381269358 389035360 7766002 0.02016345
## 104 2021-04-12 vxa 386616513 394383158 7766645 0.01988898
## 105 2021-04-13 vxa 391813947 399581255 7767308 0.01962940
## 106 2021-04-14 vxa 396910219 404680983 7770764 0.01938835
## 107 2021-04-15 vxa 404037086 411808985 7771899 0.01905237
## 108 2021-04-16 vxa 412044322 419819504 7775182 0.01869340
## 109 2020-12-29 vxa 5446606 5548984 102378 0.01862165
## 110 2021-04-17 vxa 419305065 427082242 7777177 0.01837735
## 111 2021-04-18 vxa 426459074 434238347 7779273 0.01807667
## 112 2021-04-19 vxa 430853470 438634220 7780750 0.01789732
## 113 2021-04-20 vxa 434474900 442255867 7780967 0.01774996
## 114 2021-04-21 vxa 439661586 447443801 7782215 0.01754519
## 115 2021-04-22 vxa 445719007 453502997 7783990 0.01731272
## 116 2021-04-23 vxa 452532096 460318300 7786204 0.01705910
## 117 2021-04-24 vxa 459239405 467028480 7789075 0.01681819
## 118 2021-04-25 vxa 465351768 473143258 7791490 0.01660422
## 119 2021-04-26 vxa 469586264 477377856 7791592 0.01645594
## 120 2021-04-27 vxa 472869743 480661468 7791725 0.01634288
## 121 2021-04-28 vxa 477382947 485175761 7792814 0.01619187
## 122 2021-04-29 vxa 482880799 490676060 7795261 0.01601398
## 123 2021-04-30 vxa 488529503 496326898 7797395 0.01583458
## 124 2021-05-01 vxa 495180084 502980017 7799933 0.01562862
## 125 2021-05-02 vxa 499489412 507291071 7801659 0.01549823
## 126 2021-05-03 vxa 501898168 509700681 7802513 0.01542610
## 127 2021-05-04 vxa 503882036 511684721 7802685 0.01536617
## 128 2021-05-05 vxa 507513496 515317513 7804017 0.01525964
## 129 2021-05-06 vxa 512372989 520178645 7805656 0.01511916
## 130 2021-05-07 vxa 518024858 525833299 7808441 0.01496073
## 131 2021-05-08 vxa 523201251 531012992 7811741 0.01482003
## 132 2021-05-09 vxa 527985953 535799586 7813633 0.01469024
## 133 2021-05-10 vxa 531759403 539573811 7814408 0.01458819
## 134 2021-05-11 vxa 534834342 542649370 7815028 0.01450607
## 135 2021-05-12 vxa 537960795 545776624 7815829 0.01442384
## 136 2021-05-13 vxa 541855568 549673486 7817918 0.01432471
## 137 2021-05-14 vxa 545571636 553393155 7821519 0.01423434
## 138 2021-05-15 vxa 550398046 558222357 7824311 0.01411540
## 139 2021-05-16 vxa 554623659 562449840 7826181 0.01401194
## 140 2021-05-17 vxa 557607976 565435800 7827824 0.01394037
## 141 2021-05-18 vxa 559857841 567685925 7828084 0.01388520
## 142 2020-12-28 vxa 4817252 4884299 67047 0.01382191
## 143 2021-05-19 vxa 563394524 571224729 7830205 0.01380235
## 144 2021-05-20 vxa 567647619 575479362 7831743 0.01370232
## 145 2021-05-21 vxa 572083034 579917747 7834713 0.01360192
## 146 2021-05-22 vxa 576809703 584647111 7837408 0.01349582
## 147 2021-05-23 vxa 580400750 588239955 7839205 0.01341594
## 148 2021-05-24 vxa 582749803 590589971 7840168 0.01336385
## 149 2021-05-25 vxa 584547054 592383117 7836063 0.01331611
## 150 2021-05-26 vxa 587421133 595259411 7838278 0.01325511
## 151 2020-12-27 vxa 4504812 4564760 59948 0.01321959
## 152 2021-05-27 vxa 590472787 598313357 7840570 0.01319088
## 153 2021-05-28 vxa 593258570 601101246 7842676 0.01313285
## 154 2021-05-29 vxa 596503808 604348968 7845160 0.01306598
## 155 2021-05-30 vxa 598969041 606816105 7847064 0.01301569
## 156 2021-05-31 vxa 600896395 608744277 7847882 0.01297556
## 157 2021-06-01 vxa 601923654 609771995 7848341 0.01295431
## 158 2021-06-02 vxa 602942370 610790920 7848550 0.01293291
## 159 2021-06-03 vxa 604585211 612434670 7849459 0.01289948
## 160 2020-12-24 vxa 3395582 3439504 43922 0.01285192
## 161 2021-06-04 vxa 607408409 615258306 7849897 0.01284062
## 162 2021-06-05 vxa 609728428 617580696 7852268 0.01279591
## 163 2021-06-06 vxa 612202377 620057995 7855618 0.01274993
## 164 2021-06-07 vxa 614926685 622782869 7856184 0.01269471
## 165 2021-06-08 vxa 617073291 624929490 7856199 0.01265086
## 166 2021-06-09 vxa 618754682 626611258 7856576 0.01261730
## 167 2021-06-10 vxa 620644278 628501824 7857546 0.01258067
## 168 2021-06-11 vxa 622290256 630149528 7859272 0.01255034
## 169 2021-06-12 vxa 625518205 633379718 7861513 0.01248952
## 170 2021-06-13 vxa 627960480 635824147 7863667 0.01244463
## 171 2021-06-14 vxa 630626715 638491078 7864363 0.01239343
## 172 2021-06-15 vxa 633115319 640981695 7866376 0.01234816
## 173 2021-06-16 vxa 635177490 643043939 7866449 0.01230843
## 174 2021-06-17 vxa 639314129 647184023 7869894 0.01223460
## 175 2021-06-18 vxa 641491889 649363524 7871635 0.01219600
## 176 2021-06-19 vxa 643648821 651522305 7873484 0.01215821
## 177 2021-06-20 vxa 645359689 653233746 7874057 0.01212705
## 178 2021-06-21 vxa 646592551 654472673 7880122 0.01211334
## 179 2021-06-22 vxa 647888063 655768668 7880605 0.01209000
## 180 2021-06-23 vxa 649193960 657076451 7882491 0.01206870
## 181 2021-06-24 vxa 650844276 658728749 7884473 0.01204129
## 182 2021-06-25 vxa 651883171 659768925 7885754 0.01202415
## 183 2021-06-26 vxa 653746142 661633477 7887335 0.01199248
## 184 2021-06-27 vxa 656160404 664049390 7888986 0.01195111
## 185 2021-06-28 vxa 658383276 666272326 7889050 0.01191110
## 186 2021-06-29 vxa 659901032 667790635 7889603 0.01188469
## 187 2020-12-26 vxa 4380828 4433001 52173 0.01183890
## 188 2021-06-30 vxa 662676560 670567209 7890649 0.01183677
## 189 2021-07-01 vxa 665963644 673855029 7891385 0.01177978
## 190 2021-07-02 vxa 667291980 675184975 7892995 0.01175885
## 191 2020-12-25 vxa 4067872 4115853 47981 0.01172596
## 192 2021-07-03 vxa 669628324 677521708 7893384 0.01171864
## 193 2021-07-04 vxa 670929526 678824343 7894817 0.01169816
## 194 2021-07-05 vxa 671735164 679630015 7894851 0.01168426
## 195 2021-07-06 vxa 672199146 680094016 7894870 0.01167627
## 196 2021-07-07 vxa 673074052 680969411 7895359 0.01166190
## 197 2021-07-08 vxa 674475410 682372114 7896704 0.01163978
## 198 2021-07-09 vxa 675728954 683626952 7897998 0.01162021
## 199 2021-07-10 vxa 676937142 684836241 7899099 0.01160119
## 200 2021-07-11 vxa 678122716 686022381 7899665 0.01158185
## 201 2021-07-12 vxa 679023069 686923718 7900649 0.01156802
## 202 2021-07-13 vxa 679708908 687610091 7901183 0.01155719
## 203 2021-07-14 vxa 680809727 688711439 7901712 0.01153938
## 204 2021-07-15 vxa 681954772 689857403 7902631 0.01152145
## 205 2021-07-16 vxa 683076155 690979758 7903603 0.01150405
## 206 2021-07-17 vxa 684358433 692262907 7904474 0.01148388
## 207 2021-07-18 vxa 685371906 693277651 7905745 0.01146882
## 208 2021-07-19 vxa 686388090 694294317 7906227 0.01145264
## 209 2021-07-20 vxa 686759099 694665840 7906741 0.01144722
## 210 2021-07-21 vxa 688016571 695924500 7907929 0.01142813
## 211 2021-07-22 vxa 689350784 697259348 7908564 0.01140705
## 212 2021-07-23 vxa 690563454 698458911 7895457 0.01136837
## 213 2021-07-24 vxa 691926058 699823425 7897367 0.01134883
## 214 2021-07-25 vxa 693498090 701397151 7899061 0.01132567
## 215 2021-07-26 vxa 694285530 702184652 7899122 0.01131298
## 216 2021-07-27 vxa 695077881 702977705 7899824 0.01130116
## 217 2021-07-28 vxa 696593467 704494043 7900576 0.01127778
## 218 2021-07-29 vxa 698030758 705932348 7901590 0.01125612
## 219 2021-07-30 vxa 699757751 707660172 7902421 0.01122967
## 220 2021-07-31 vxa 701197062 709100426 7903364 0.01120808
## 221 2021-08-01 vxa 702844466 710748186 7903720 0.01118246
## 222 2021-08-02 vxa 703782987 711687872 7904885 0.01116927
## 223 2021-08-03 vxa 704690674 712596258 7905584 0.01115594
## 224 2021-08-04 vxa 706154074 714060713 7906639 0.01113443
## 225 2021-08-05 vxa 707898247 715805607 7907360 0.01110815
## 226 2021-08-06 vxa 709556535 717464760 7908225 0.01108354
## 227 2021-08-07 vxa 711249564 719158886 7909322 0.01105883
## 228 2021-08-08 vxa 712811659 720722021 7910362 0.01103617
## 229 2021-08-09 vxa 713878648 721789066 7910418 0.01101985
## 230 2021-08-10 vxa 715115736 723026669 7910933 0.01100160
## 231 2021-08-11 vxa 716438666 724350295 7911629 0.01098236
## 232 2021-08-12 vxa 717763984 725676500 7912516 0.01096341
## 233 2021-08-13 vxa 719616893 727530633 7913740 0.01093702
## 234 2021-08-14 vxa 721621667 729536564 7914897 0.01090839
## 235 2021-08-15 vxa 722964598 730880861 7916263 0.01089010
## 236 2021-08-16 vxa 724687252 732604643 7917391 0.01086590
## 237 2021-08-17 vxa 725894030 733812644 7918614 0.01084960
## 238 2021-08-18 vxa 727318151 735237507 7919356 0.01082948
## 239 2021-08-19 vxa 729380316 737300431 7920115 0.01080005
## 240 2021-08-20 vxa 731420314 739341628 7921314 0.01077171
## 241 2021-08-21 vxa 733537317 741460274 7922957 0.01074301
## 242 2021-08-22 vxa 735499881 743423328 7923447 0.01071516
## 243 2021-08-23 vxa 736729701 744653478 7923777 0.01069781
## 244 2021-08-24 vxa 738031476 745957425 7925949 0.01068195
## 245 2021-08-25 vxa 739911752 747838251 7926499 0.01065569
## 246 2021-08-26 vxa 741787924 749715346 7927422 0.01063011
## 247 2021-08-27 vxa 743959352 751887925 7928573 0.01060078
## 248 2021-08-28 vxa 746134440 754064442 7930002 0.01057193
## 249 2021-08-29 vxa 748071410 756002444 7931034 0.01054607
## 250 2021-08-30 vxa 749467709 757399811 7932102 0.01052794
## 251 2021-08-31 vxa 750785914 758719509 7933595 0.01051152
## 252 2021-09-01 vxa 752949263 760883856 7934593 0.01048278
## 253 2021-09-02 vxa 754649632 762585018 7935386 0.01046033
## 254 2021-09-03 vxa 757482897 765421059 7938162 0.01042503
## 255 2021-09-04 vxa 759467604 767406259 7938655 0.01039857
## 256 2021-09-05 vxa 760691321 768630055 7938734 0.01038203
## 257 2021-09-06 vxa 761511343 769450114 7938771 0.01037096
## 258 2021-09-07 vxa 762523380 770463185 7939805 0.01035861
## 259 2021-09-08 vxa 764454842 772397079 7942237 0.01033572
## 260 2021-09-09 vxa 765824157 773767414 7943257 0.01031865
## 261 2021-09-10 vxa 767758097 775703164 7945067 0.01029513
## 262 2021-09-11 vxa 769603644 777550536 7946892 0.01027292
## 263 2021-09-12 vxa 771183033 779129925 7946892 0.01025198
## 264 2021-09-13 vxa 772373795 780320687 7946892 0.01023626
## 265 2021-09-14 vxa 773630897 781579289 7948392 0.01022163
## 266 2021-09-15 vxa 775340908 783289914 7949006 0.01019999
## 267 2021-09-16 vxa 776856560 784806820 7950260 0.01018178
## 268 2021-09-17 vxa 778796072 786748835 7952763 0.01015974
## 269 2021-09-18 vxa 780656201 788610284 7954083 0.01013733
## 270 2021-09-19 vxa 782031409 789985492 7954083 0.01011959
## 271 2021-09-20 vxa 783344685 791298768 7954083 0.01010271
## 272 2021-09-21 vxa 784436200 792392616 7956416 0.01009167
## 273 2021-09-22 vxa 785887057 793844457 7957400 0.01007437
## 274 2021-09-23 vxa 786571141 794530252 7959111 0.01006781
## 275 2021-09-24 vxa 788090243 796051559 7961316 0.01005127
## 276 2021-09-25 vxa 789734467 797697345 7962878 0.01003240
## 277 2021-09-26 vxa 791247931 799210809 7962878 0.01001331##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 20,127
## Columns: 69
## $ date <date> 2021-10-24, 2021-10-24, 2021-1~
## $ MMWR_week <dbl> 43, 43, 43, 43, 43, 43, 43, 43,~
## $ state <chr> "CT", "MD", "MH", "AK", "KS", "~
## $ Distributed <dbl> 5979725, 10599110, 57140, 11306~
## $ Distributed_Janssen <dbl> 280200, 479400, 11300, 72400, 2~
## $ Distributed_Moderna <dbl> 2367920, 3951380, 43500, 444260~
## $ Distributed_Pfizer <dbl> 3331605, 6168330, 2340, 613965,~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 167721, 175317, 97821, 154553, ~
## $ Distributed_Per_100k_12Plus <dbl> 192525, 205294, 114546, 185690,~
## $ Distributed_Per_100k_18Plus <dbl> 210713, 224987, 125591, 204986,~
## $ Distributed_Per_100k_65Plus <dbl> 948795, 1104770, 577347, 123447~
## $ vxa <dbl> 5288142, 8379027, 42025, 854656~
## $ Administered_12Plus <dbl> 5286342, 8378384, 41989, 852408~
## $ Administered_18Plus <dbl> 4892169, 7741330, 41224, 793669~
## $ Administered_65Plus <dbl> 1362943, 1942596, 2381, 169707,~
## $ Administered_Janssen <dbl> 209574, 303391, 2162, 35983, 11~
## $ Administered_Moderna <dbl> 1895023, 3022877, 39107, 324896~
## $ Administered_Pfizer <dbl> 3182162, 5040646, 754, 493195, ~
## $ Administered_Unk_Manuf <dbl> 1383, 12113, 2, 582, 1594, 371,~
## $ Admin_Per_100k <dbl> 148323, 138595, 71945, 116829, ~
## $ Admin_Per_100k_12Plus <dbl> 170201, 162281, 84173, 139997, ~
## $ Admin_Per_100k_18Plus <dbl> 172390, 164325, 90608, 143895, ~
## $ Admin_Per_100k_65Plus <dbl> 216256, 202481, 24058, 185294, ~
## $ Recip_Administered <dbl> 5297329, 8439877, 42077, 849994~
## $ Administered_Dose1_Recip <dbl> 2789952, 4383458, 23753, 431503~
## $ Administered_Dose1_Pop_Pct <dbl> 78.3, 72.5, 40.7, 59.0, 61.9, 7~
## $ Administered_Dose1_Recip_12Plus <dbl> 2788575, 4382805, 23727, 430148~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 89.8, 84.9, 47.6, 70.6, 73.5, 8~
## $ Administered_Dose1_Recip_18Plus <dbl> 2579599, 4045674, 23074, 399043~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 90.9, 85.9, 50.7, 72.3, 75.8, 8~
## $ Administered_Dose1_Recip_65Plus <dbl> 650501, 940087, 1322, 80686, 48~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 99.9, 98.0, 13.4, 88.1, 99.9, 9~
## $ vxc <dbl> 2506925, 3978145, 20218, 382736~
## $ vxcpoppct <dbl> 70.3, 65.8, 34.6, 52.3, 52.9, 6~
## $ Series_Complete_12Plus <dbl> 2506422, 3977925, 20207, 381864~
## $ Series_Complete_12PlusPop_Pct <dbl> 80.7, 77.0, 40.5, 62.7, 62.8, 7~
## $ vxcgte18 <dbl> 2325581, 3682392, 20177, 355192~
## $ vxcgte18pct <dbl> 81.9, 78.2, 44.3, 64.4, 64.9, 7~
## $ vxcgte65 <dbl> 586541, 877275, 1151, 75064, 40~
## $ vxcgte65pct <dbl> 93.1, 91.4, 11.6, 82.0, 85.8, 8~
## $ Series_Complete_Janssen <dbl> 208902, 297899, 2153, 33632, 10~
## $ Series_Complete_Moderna <dbl> 889665, 1406414, 18049, 144516,~
## $ Series_Complete_Pfizer <dbl> 1407945, 2270604, 15, 204516, 8~
## $ Series_Complete_Unk_Manuf <dbl> 413, 3228, 1, 72, 496, 189, 0, ~
## $ Series_Complete_Janssen_12Plus <dbl> 208821, 297863, 2150, 33630, 10~
## $ Series_Complete_Moderna_12Plus <dbl> 889434, 1406376, 18041, 144512,~
## $ Series_Complete_Pfizer_12Plus <dbl> 1407758, 2270461, 15, 203650, 8~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 409, 3225, 1, 72, 496, 189, 0, ~
## $ Series_Complete_Janssen_18Plus <dbl> 208725, 297692, 2144, 33495, 10~
## $ Series_Complete_Moderna_18Plus <dbl> 889245, 1405935, 18019, 144126,~
## $ Series_Complete_Pfizer_18Plus <dbl> 1227300, 1975672, 13, 177504, 7~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 311, 3093, 1, 67, 434, 174, 0, ~
## $ Series_Complete_Janssen_65Plus <dbl> 20878, 53323, 100, 3222, 17510,~
## $ Series_Complete_Moderna_65Plus <dbl> 231034, 414820, 1051, 41535, 19~
## $ Series_Complete_Pfizer_65Plus <dbl> 335708, 410484, 0, 30491, 19690~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 88, 853, 0, 29, 222, 53, 0, 1, ~
## $ Additional_Doses <dbl> 171833, 285361, 9, 41549, 10797~
## $ Additional_Doses_Vax_Pct <dbl> 6.9, 7.2, 0.0, 10.9, 7.0, 6.6, ~
## $ Additional_Doses_18Plus <dbl> 171636, 284802, 9, 41381, 10773~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 7.4, 7.7, 0.0, 11.7, 7.5, 7.1, ~
## $ Additional_Doses_50Plus <dbl> 155532, 232981, 3, 29298, 90319~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 26.5, 26.6, 0.3, 39.0, 22.1, 24~
## $ Additional_Doses_65Plus <dbl> 129481, 173565, 0, 17552, 71028~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 22.1, 19.8, 0.0, 23.4, 17.4, 17~
## $ Additional_Doses_Moderna <dbl> 14201, 48318, 7, 7372, 14412, 7~
## $ Additional_Doses_Pfizer <dbl> 157304, 236541, 2, 34132, 93452~
## $ Additional_Doses_Janssen <dbl> 108, 435, 0, 38, 70, 428, 8, 36~
## $ Additional_Doses_Unk_Manuf <dbl> 220, 67, 0, 7, 38, 3, 20, 14, 0~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.16e+10 2.21e+8 4.52e+7 723299 37760
## 2 after 1.16e+10 2.20e+8 4.50e+7 719774 32640
## 3 pctchg 4.34e- 3 4.25e-3 4.62e-3 0.00487 0.136
##
##
## Processed for cdcDaily:
## Rows: 32,640
## Columns: 6
## $ date <date> 2021-09-01, 2021-01-13, 2020-07-30, 2021-02-02, 2020-05-03~
## $ state <chr> "ND", "IN", "ME", "MS", "NH", "NV", "NE", "NC", "MI", "CT",~
## $ tot_cases <dbl> 118491, 574488, 3910, 280182, 2518, 320719, 20150, 875359, ~
## $ tot_deaths <dbl> 1562, 10920, 123, 6730, 86, 5530, 282, 12363, 0, 3285, 4806~
## $ new_cases <dbl> 536, 3654, 22, 1059, 89, 180, 179, 1614, 0, 621, 2750, 1446~
## $ new_deaths <dbl> 1, 74, 2, 13, 2, 0, -1, 16, 0, 66, 40, 33, 0, 16, 15, 162, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.48e+7 2.87e+7 634013 32249
## 2 after 3.46e+7 2.86e+7 620096 31014
## 3 pctchg 5.25e-3 5.12e-3 0.0220 0.0383
##
##
## Processed for cdcHosp:
## Rows: 31,014
## Columns: 5
## $ date <date> 2020-10-13, 2020-10-12, 2020-10-11, 2020-10-11, 2020-10-10~
## $ state <chr> "NH", "NH", "HI", "NM", "HI", "HI", "MD", "NC", "ID", "ND",~
## $ inp <dbl> 34, 32, 99, 171, 127, 110, 756, 1427, 191, 194, 168, 91, 47~
## $ hosp_adult <dbl> 34, 31, 99, 166, 125, 108, 721, 1392, 189, 190, 166, 90, 47~
## $ hosp_ped <dbl> 0, 1, 0, 5, 2, 2, 9, 35, 2, 4, 2, 1, 0, 1, 1, 9, 11, 3, 6, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.46e+11 6.44e+10 573778. 1.88e+10 958324. 6.16e+10 697087.
## 2 after 7.06e+10 3.11e+10 483274. 9.10e+ 9 870801. 2.98e+10 595268.
## 3 pctchg 5.16e- 1 5.16e- 1 0.158 5.16e- 1 0.0913 5.17e- 1 0.146
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 16,065
## Columns: 9
## $ date <date> 2021-10-24, 2021-10-24, 2021-10-24, 2021-10-24, 2021-10-2~
## $ state <chr> "CT", "MD", "AK", "KS", "NJ", "NV", "NE", "HI", "KY", "PA"~
## $ vxa <dbl> 5288142, 8379027, 854656, 3270476, 12082885, 3573760, 2287~
## $ vxc <dbl> 2506925, 3978145, 382736, 1539866, 5861950, 1617387, 10822~
## $ vxcpoppct <dbl> 70.3, 65.8, 52.3, 52.9, 66.0, 52.5, 55.9, 59.5, 54.5, 60.1~
## $ vxcgte65 <dbl> 586541, 877275, 75064, 407923, 1290147, 390112, 277232, 23~
## $ vxcgte65pct <dbl> 93.1, 91.4, 82.0, 85.8, 87.4, 78.7, 88.7, 87.2, 85.2, 90.1~
## $ vxcgte18 <dbl> 2325581, 3682392, 355192, 1436087, 5448479, 1521578, 10084~
## $ vxcgte18pct <dbl> 81.9, 78.2, 64.4, 64.9, 78.5, 63.7, 69.2, 71.0, 66.2, 71.4~
##
## Integrated per capita data file:
## Rows: 32,904
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211024)The post-processing steps are then run;
# Create pivoted burden data
burdenPivotList_211024 <- postProcessCDCDaily(cdc_daily_211024,
dataThruLabel="Oct 23, 2021",
keyDatesBurden=c("2021-10-23", "2021-04-23",
"2020-10-23", "2020-04-23"
),
keyDatesVaccine=c("2021-10-23", "2021-08-23",
"2021-06-23", "2021-04-23"
),
returnData=TRUE
)## Joining, by = "state"##
## *** File has been checked for uniqueness by: state date name## Warning: Removed 24 row(s) containing missing values (geom_path).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 24 rows containing missing values (position_stack).
## Warning: Removed 9 row(s) containing missing values (geom_path).
## Warning: Removed 51 rows containing missing values (geom_col).## Warning: Removed 51 rows containing missing values (geom_text).## Warning: Removed 51 rows containing missing values (geom_col).## Warning: Removed 51 rows containing missing values (geom_text).
# Create hospitalized per capita data
hospAgePerCapita(dfStateAgeBucket,
lst=burdenPivotList_211024,
popVar="pop2019",
excludeState=c("ND"),
cumStartDate="2020-07-15"
)## Warning: Removed 18 row(s) containing missing values (geom_path).
## # A tibble: 71,591 x 8
## state date bucket03 value pop vpm vpm7 vpmcum
## <chr> <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NV 2020-01-02 0-19 7 760090 9.21 NA 9.21
## 2 NV 2020-01-02 20-59 80 1627729 49.1 NA 49.1
## 3 NV 2020-01-02 60+ 156 681510 229. NA 229.
## 4 NV 2020-01-03 0-19 7 760090 9.21 NA 18.4
## 5 NV 2020-01-03 20-59 80 1627729 49.1 NA 98.3
## 6 NV 2020-01-03 60+ 156 681510 229. NA 458.
## 7 AR 2020-01-04 0-19 0 778802 0 NA 0
## 8 AR 2020-01-04 20-59 0 1521886 0 NA 0
## 9 AR 2020-01-04 60+ 0 711854 0 NA 0
## 10 NV 2020-01-04 0-19 7 760090 9.21 NA 27.6
## # ... with 71,581 more rows# Create CFR plots for select states
cfrStates <- list("FL"=list(keyState="FL", minDate="2020-08-01", multDeath=70),
"LA"=list(keyState="LA", minDate="2020-08-01", multDeath=80),
"CA"=list(keyState="CA", minDate="2020-08-01", multDeath=100),
"IL"=list(keyState="IL", minDate="2020-08-01", multDeath=100)
)
purrr::walk(cfrStates, .f=function(x) onePageCFRPlot(burdenPivotList_211024$dfPivot,
keyState=x$keyState,
minDate=x$minDate,
multDeath=x$multDeath
)
)
A function is written to find local peaks in a vector. Peaks are defined as local maxima for a given window:
# Function to find local extrema in a vector
findPeaks <- function(x,
width=1,
align="center",
FUN=max,
gt=if(identical(FUN, max)) 0 else NULL,
lt=if(identical(FUN, min)) 0 else NULL,
fillVal=if(identical(FUN, max)) gt else if(identical(FUN, min)) lt else NA,
epsTol=1e-12,
returnBool=TRUE,
...
) {
# FUNCTION ARGUMENTS:
# x: a numeric vector
# width: the width of the window to use
# align: whether the window should be "center", "left", or "right"
# FUN: the function to be used (max to find peaks, min to find valleys)
# gt: to be defined, the value must be greater than gt (NULL means use any value)
# lt: to be defined, the value must be less than lt (NULL means use any value)
# fillVal: value to use as output if a window does not exist (too close to boundary)
# epsTol: the epsilon value for considering two values to be the same
# returnBool: should the boolean be returned? TRUE means return TRUE/FALSE for peaks, FALSE means return vector
# ...: any other arguments to be passed to zoo::rollapply()
# Create the rolling data
rolls <- zoo::rollapply(x, width=width, align=align, FUN=FUN, fill=fillVal, ...)
# No post-processing applied unless returnBool is TRUE
if(!isTRUE(returnBool)) return(rolls)
# Post-processing managed for gt and lt
if(!is.null(gt)) rolls <- ifelse(rolls<=gt, NA, rolls)
if(!is.null(lt)) rolls <- ifelse(rolls>=lt, NA, rolls)
# Return the boolean vector
!is.na(rolls) & (abs(rolls-x) <= epsTol)
}
# Testing on a sinusoidal sequence
sinX <- seq(0, 10*pi, by=0.01*pi)
ggplot(tibble::tibble(x=sinX, y=sin(sinX)), aes(x=x, y=y)) +
geom_line() +
geom_point(data=~filter(tibble::tibble(x=sinX, y=sin(sinX), z=findPeaks(y, width=21)), z),
color="green",
size=5
) +
geom_point(data=~filter(tibble::tibble(x=sinX, y=sin(sinX), z=findPeaks(y, width=21, FUN=min)), z),
color="red",
size=5
) +
labs(x=NULL,
y=NULL,
title="Example of maxima and minima for a sin curve",
subtitle="Green (max) and Red (min)"
)
# Testing on national deaths data
cdc_daily_211024$dfPerCapita %>%
group_by(date) %>%
summarize(deaths=sum(new_deaths, na.rm=TRUE), .groups="drop") %>%
mutate(deaths7=zoo::rollmean(deaths, k=7, fill=NA),
isPeak=findPeaks(deaths7, width=29, gt=1),
isValley=findPeaks(deaths7, width=29, FUN=min, gt=1, lt=NULL, fillVal=NA)
) %>%
ggplot(aes(x=date, y=deaths7)) +
geom_line() +
geom_point(data=~filter(., isPeak), color="red", size=3) +
geom_text(data=~filter(., isPeak), aes(y=deaths7+100, label=date), color="red", size=3) +
geom_point(data=~filter(., isValley), color="green", size=3) +
geom_text(data=~filter(., isValley), aes(y=deaths7-100, label=date), color="black", size=3) +
labs(x=NULL,
y="Rolling 7-day mean deaths",
title="US COVID deaths peaks and valleys",
subtitle="Red (peaks) and green (valleys)"
)## Warning: Removed 6 row(s) containing missing values (geom_path).
The peaks can be calculated by census region with values for the peak and valley included:
cdc_daily_211024$dfPerCapita %>%
mutate(region=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
group_by(region, date) %>%
summarize(deaths=sum(new_deaths, na.rm=TRUE), .groups="drop") %>%
group_by(region) %>%
mutate(deaths7=zoo::rollmean(deaths, k=7, fill=NA),
isPeak=findPeaks(deaths7, width=71, gt=1),
isValley=findPeaks(deaths7, width=71, FUN=min, gt=1, lt=NULL, fillVal=NA)
) %>%
ungroup() %>%
ggplot(aes(x=date, y=deaths7)) +
geom_line() +
geom_point(data=~filter(., isPeak), color="red", size=3) +
geom_text(data=~filter(., isPeak),
aes(y=deaths7+100, label=paste0(date, "\n", round(deaths))),
color="red",
size=3
) +
geom_point(data=~filter(., isValley), color="green", size=3) +
geom_text(data=~filter(., isValley),
aes(y=deaths7-100, label=paste0(date, "\n", round(deaths))),
color="black",
size=3
) +
labs(x=NULL,
y="Rolling 7-day mean deaths",
title="US COVID deaths peaks and valleys",
subtitle="Red (peaks) and green (valleys)"
) +
facet_wrap(~region, scales="free_y")## Warning: Removed 6 row(s) containing missing values (geom_path).
A similar approach can be run for states of at least 5 million people:
cdc_daily_211024$dfPerCapita %>%
filter(state %in% (getStateData() %>% filter(pop >= 5000000) %>% pull(state))) %>%
group_by(state, date) %>%
summarize(deaths=sum(new_deaths, na.rm=TRUE), .groups="drop") %>%
group_by(state) %>%
mutate(deaths7=zoo::rollmean(deaths, k=7, fill=NA),
isPeak=findPeaks(deaths7, width=71, gt=1),
isValley=findPeaks(deaths7, width=71, FUN=min, gt=1, lt=NULL, fillVal=NA)
) %>%
ungroup() %>%
ggplot(aes(x=date, y=deaths7)) +
geom_line() +
geom_point(data=~filter(., isPeak), color="red", size=3) +
geom_text(data=~filter(., isPeak),
aes(y=deaths7+100, label=paste0(date, "\n", round(deaths))),
color="red",
size=3
) +
geom_point(data=~filter(., isValley), color="green", size=3) +
geom_text(data=~filter(., isValley),
aes(y=deaths7-100, label=paste0(date, "\n", round(deaths))),
color="black",
size=3
) +
labs(x=NULL,
y="Rolling 7-day mean deaths",
title="US COVID deaths peaks and valleys",
subtitle="Red (peaks) and green (valleys)"
) +
facet_wrap(~state, scales="free_y")## Warning: Removed 6 row(s) containing missing values (geom_path).
There is some noise to the state-level data, but the approach is generally identifying the right peaks and valleys. Next steps are to add post-processing so that certain peaks and valleys (such as a repeating value) are filtered. The approach is applied to US hospitalizations data:
cdc_daily_211024$dfPerCapita %>%
group_by(date) %>%
summarize(hosp=sum(inp, na.rm=TRUE), .groups="drop") %>%
mutate(hosp7=zoo::rollmean(hosp, k=7, fill=NA),
isPeak=findPeaks(hosp7, width=71, gt=1),
isValley=findPeaks(hosp7, width=71, FUN=min, gt=1, lt=NULL, fillVal=NA)
) %>%
ungroup() %>%
ggplot(aes(x=date, y=hosp7/1000)) +
geom_line() +
geom_point(data=~filter(., isPeak), color="red", size=3) +
geom_text(data=~filter(., isPeak),
aes(y=hosp7/1000+10, label=paste0(date, "\n", round(hosp7/1000, 1))),
color="red",
size=3
) +
geom_point(data=~filter(., isValley), color="green", size=3) +
geom_text(data=~filter(., isValley),
aes(y=hosp7/1000-10, label=paste0(date, "\n", round(hosp7/1000, 1))),
color="black",
size=3
) +
labs(x=NULL,
y="Rolling 7-day mean hospitalized (000)",
title="US COVID hospitalized (000) peaks and valleys",
subtitle="Red (peaks) and green (valleys)"
)## Warning: Removed 6 row(s) containing missing values (geom_path).
A function is written to allow for different variables and facets to be used:
makePeakValley <- function(df,
numVar,
windowWidth,
rollMean=NULL,
uqBy=c("date"),
facetVar=c(),
fnNumVar=function(x) x,
fnPeak=function(x) x+100,
fnValley=function(x) x-100,
useTitle="",
yLab=""
) {
# FUNCTION ARGUMENTS
# df: a data frame or tibble
# numVar: the numeric variable of interest
# windowWidth: width of the window for calculating peaks and valleys
# rollMean: the number of days for rolling mean (NULL means no rolling mean)
# uqBy: variable that the resutling data should be unique by
# facetVar: variable for faceting (c() means no facets)
# fnNumVar: what function should be applied to numVar (e.g., function(x) x/1000)
# fnPeak: function for plotting the peak labels
# fnValley: function for plotting the valley labels
# useTitle: title for plots
# yLab: y-axis label for plots
# Create named vectors for useTitle and yLab if not passed
if(is.null(names(useTitle)))
useTitle <- rep(useTitle, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
if(is.null(names(yLab)))
yLab <- rep(yLab, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
# Create named lists for fnNumVar, fnPeak, and fnValley
tempMakeList <- function(f, n, nms) {
tempList <- vector("list", length=n)
for(a in 1:n) tempList[[a]] <- f
names(tempList) <- nms
tempList
}
if(is.null(names(fnNumVar))) fnNumVar <- tempMakeList(fnNumVar, n=length(numVar), nms=numVar)
if(is.null(names(fnPeak))) fnPeak <- tempMakeList(fnPeak, n=length(numVar), nms=numVar)
if(is.null(names(fnValley))) fnValley <- tempMakeList(fnValley, n=length(numVar), nms=numVar)
# Create the relevant data frame
newDF <- df %>%
group_by_at(all_of(c(uqBy, facetVar))) %>%
summarize(across(all_of(numVar), .fns=sum, na.rm=TRUE), .groups="drop") %>%
group_by_at(all_of(facetVar)) %>%
mutate(if(!is.null(rollMean)) across(all_of(numVar), .fns=zoo::rollmean, k=rollMean, fill=NA),
across(all_of(numVar), .fns=findPeaks, width=windowWidth, gt=1, .names="{.col}_isPeak"),
across(all_of(numVar),
.fns=findPeaks,
width=windowWidth,
FUN=min,
gt=1,
lt=NULL,
fillVal=NA
,.names="{.col}_isValley"
)
) %>%
ungroup()
# Create the relevant plots
for(keyVar in numVar) {
p1 <- newDF %>%
ggplot(aes(x=get(uqBy), y=fnNumVar[[keyVar]](get(keyVar)))) +
geom_line() +
geom_point(data=~filter(., get(paste0(keyVar, "_isPeak"))), color="red", size=3) +
geom_point(data=~filter(., get(paste0(keyVar, "_isValley"))), color="green", size=3) +
geom_text(data=~filter(., get(paste0(keyVar, "_isPeak"))),
aes(y=fnPeak[[keyVar]](fnNumVar[[keyVar]](get(keyVar))),
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="red",
size=3
) +
geom_text(data=~filter(., get(paste0(keyVar, "_isValley"))),
aes(y=fnValley[[keyVar]](fnNumVar[[keyVar]](get(keyVar))),
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="black",
size=3
) +
labs(x=NULL,
y=yLab[[keyVar]],
title=useTitle[[keyVar]],
subtitle="Red (peaks) and green (valleys)"
)
if(length(facetVar) > 0) p1 <- p1 + facet_wrap(~get(facetVar), scales="free_y")
print(p1)
}
return(newDF)
}
cdc_daily_211024$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=function(x) x/1000,
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths (000)",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 2,631 x 11
## date regn new_deaths new_cases inp new_deaths_isPe~ new_cases_isPeak
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 2020-01-01 Nort~ NA NA NA FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE
## 4 2020-01-02 Nort~ NA NA NA FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE
## 7 2020-01-03 Nort~ NA NA NA FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE
## 10 2020-01-04 Nort~ 0 0 0 FALSE FALSE
## # ... with 2,621 more rows, and 4 more variables: inp_isPeak <lgl>,
## # new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl>cdc_daily_211024$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 2,631 x 11
## date regn new_deaths new_cases inp new_deaths_isPe~ new_cases_isPeak
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 2020-01-01 Nort~ NA NA NA FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE
## 4 2020-01-02 Nort~ NA NA NA FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE
## 7 2020-01-03 Nort~ NA NA NA FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE
## 10 2020-01-04 Nort~ 0 0 0 FALSE FALSE
## # ... with 2,621 more rows, and 4 more variables: inp_isPeak <lgl>,
## # new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl>The same process is run with the vaccinations data:
cdc_daily_211024$dfPerCapita %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=15,
facetVar=c("regn"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+50,
"vxc"=function(x) x+50
),
fnValley=list("vxa"=function(x) x-50,
"vxc"=function(x) x-50
),
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 15-day mean administered (000)",
"vxc"="Rolling 15-day mean completed (000)"
)
)## Warning: Removed 14 row(s) containing missing values (geom_path).
## Warning: Removed 14 row(s) containing missing values (geom_path).
## # A tibble: 1,312 x 8
## date regn vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 North~ NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 North~ NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 South NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 West NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-02 North~ NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-02 North~ NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-02 South NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-02 West NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-03 North~ NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-03 North~ NA NA FALSE FALSE FALSE FALSE
## # ... with 1,302 more rowsThe process is repeated, focused only on states of at least 8 million people:
cdc_daily_211024$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25,
"vxc"=function(x) x+25
),
fnValley=list("vxa"=function(x) x-25,
"vxc"=function(x) x-25
),
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 21-day mean administered (000)",
"vxc"="Rolling 21-day mean completed (000)"
)
)## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 3,936 x 8
## date state vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 3,926 more rowsNext steps are to allow for a custom function by facet, to allow for labels in different positions when facets have different ranges:
makePeakValley <- function(df,
numVar,
windowWidth,
rollMean=NULL,
uqBy=c("date"),
facetVar=c(),
fnNumVar=function(x) x,
fnPeak=function(x) x+100,
fnValley=function(x) x-100,
fnGroupFacet=FALSE,
useTitle="",
yLab=""
) {
# FUNCTION ARGUMENTS
# df: a data frame or tibble
# numVar: the numeric variable of interest
# windowWidth: width of the window for calculating peaks and valleys
# rollMean: the number of days for rolling mean (NULL means no rolling mean)
# uqBy: variable that the resutling data should be unique by
# facetVar: variable for faceting (c() means no facets)
# fnNumVar: what function should be applied to numVar (e.g., function(x) x/1000)
# fnPeak: function for plotting the peak labels
# fnValley: function for plotting the valley labels
# fnGroupFacet: boolean, should the functions be run separatelt for each facet as a grouping variable?
# useful for labeling if the goal is to use 0.1*max(yVar) rather than a global peak and valley
# useTitle: title for plots
# yLab: y-axis label for plots
# Create named vectors for useTitle and yLab if not passed
if(is.null(names(useTitle)))
useTitle <- rep(useTitle, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
if(is.null(names(yLab)))
yLab <- rep(yLab, times=length(numVar)) %>% purrr::set_names(all_of(numVar))
# Create named lists for fnNumVar, fnPeak, and fnValley
tempMakeList <- function(f, n, nms) {
tempList <- vector("list", length=n)
for(a in 1:n) tempList[[a]] <- f
names(tempList) <- nms
tempList
}
if(is.null(names(fnNumVar))) fnNumVar <- tempMakeList(fnNumVar, n=length(numVar), nms=numVar)
if(is.null(names(fnPeak))) fnPeak <- tempMakeList(fnPeak, n=length(numVar), nms=numVar)
if(is.null(names(fnValley))) fnValley <- tempMakeList(fnValley, n=length(numVar), nms=numVar)
# Create the relevant data frame
newDF <- df %>%
group_by_at(all_of(c(uqBy, facetVar))) %>%
summarize(across(all_of(numVar), .fns=sum, na.rm=TRUE), .groups="drop") %>%
group_by_at(all_of(facetVar)) %>%
mutate(if(!is.null(rollMean)) across(all_of(numVar), .fns=zoo::rollmean, k=rollMean, fill=NA),
across(all_of(numVar), .fns=findPeaks, width=windowWidth, gt=1, .names="{.col}_isPeak"),
across(all_of(numVar),
.fns=findPeaks,
width=windowWidth,
FUN=min,
gt=1,
lt=NULL,
fillVal=NA
,.names="{.col}_isValley"
)
) %>%
ungroup()
# Group by the facet variable(s) if not NULL and separate function by facet requested
if(!is.null(facetVar) & isTRUE(fnGroupFacet)) newDF <- newDF %>% group_by_at(all_of(facetVar))
# Create the relevant plots
for(keyVar in numVar) {
p1 <- newDF %>%
mutate(posPeak=fnPeak[[keyVar]](fnNumVar[[keyVar]](get(keyVar))),
posValley=fnValley[[keyVar]](fnNumVar[[keyVar]](get(keyVar)))
) %>%
ggplot(aes(x=get(uqBy), y=fnNumVar[[keyVar]](get(keyVar)))) +
geom_line() +
geom_point(data=~filter(., get(paste0(keyVar, "_isPeak"))), color="red", size=3) +
geom_point(data=~filter(., get(paste0(keyVar, "_isValley"))), color="green", size=3) +
geom_text(data=~filter(., get(paste0(keyVar, "_isPeak"))),
aes(y=posPeak,
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="red",
size=3
) +
geom_text(data=~filter(., get(paste0(keyVar, "_isValley"))),
aes(y=posValley,
label=paste0(get(uqBy), "\n", round(fnNumVar[[keyVar]](get(keyVar))))
),
color="black",
size=3
) +
labs(x=NULL,
y=yLab[[keyVar]],
title=useTitle[[keyVar]],
subtitle="Red (peaks) and green (valleys)"
)
if(length(facetVar) > 0) p1 <- p1 + facet_wrap(~get(facetVar), scales="free_y")
print(p1)
}
# Return the data, removing any grouping
newDF %>% ungroup()
}
# Original format for burden
cdc_daily_211024$dfPerCapita %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
makePeakValley(numVar=c("new_deaths", "new_cases", "inp"),
windowWidth = 71,
rollMean=7,
facetVar=c("regn"),
fnNumVar=list("new_deaths"=function(x) x,
"new_cases"=function(x) x/1000,
"inp"=function(x) x/1000
),
fnPeak=list("new_deaths"=function(x) x+100,
"new_cases"=function(x) x+10,
"inp"=function(x) x+10
),
fnValley=list("new_deaths"=function(x) x-100,
"new_cases"=function(x) x-5,
"inp"=function(x) x-5
),
useTitle=c("new_deaths"="US coronavirus deaths",
"new_cases"="US coronavirus cases",
"inp"="US coronavirus total hospitalized"
),
yLab=c("new_deaths"="Rolling 7-day mean deaths",
"new_cases"="Rolling 7-day mean cases (000)",
"inp"="Rolling 7-day mean in hospital (000)"
)
)## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## Warning: Removed 6 row(s) containing missing values (geom_path).
## # A tibble: 2,631 x 11
## date regn new_deaths new_cases inp new_deaths_isPe~ new_cases_isPeak
## <date> <chr> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 2020-01-01 Nort~ NA NA NA FALSE FALSE
## 2 2020-01-01 South NA NA NA FALSE FALSE
## 3 2020-01-01 West NA NA NA FALSE FALSE
## 4 2020-01-02 Nort~ NA NA NA FALSE FALSE
## 5 2020-01-02 South NA NA NA FALSE FALSE
## 6 2020-01-02 West NA NA NA FALSE FALSE
## 7 2020-01-03 Nort~ NA NA NA FALSE FALSE
## 8 2020-01-03 South NA NA NA FALSE FALSE
## 9 2020-01-03 West NA NA NA FALSE FALSE
## 10 2020-01-04 Nort~ 0 0 0 FALSE FALSE
## # ... with 2,621 more rows, and 4 more variables: inp_isPeak <lgl>,
## # new_deaths_isValley <lgl>, new_cases_isValley <lgl>, inp_isValley <lgl># Modified format - vaccinations by state
cdc_daily_211024$dfPerCapita %>%
inner_join(getStateData(), by=c("state")) %>%
filter(pop >= 8000000) %>%
select(date, state, vxa, vxc) %>%
arrange(date, state) %>%
group_by(state) %>%
mutate(across(c(vxa, vxc), .fns=function(x) x-lag(x))) %>%
ungroup() %>%
mutate(regn=c(as.character(state.region), "South")[match(state, c(state.abb, "DC"))]) %>%
filter(date >= "2020-12-01") %>%
makePeakValley(numVar=c("vxc", "vxa"),
windowWidth = 29,
rollMean=21,
facetVar=c("state"),
fnNumVar=list("vxa"=function(x) x/1000,
"vxc"=function(x) x/1000
),
fnPeak=list("vxa"=function(x) x+25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x+25*max(x, na.rm=TRUE)/400
),
fnValley=list("vxa"=function(x) x-25*max(x, na.rm=TRUE)/400,
"vxc"=function(x) x-25*max(x, na.rm=TRUE)/400
),
fnGroupFacet=TRUE,
useTitle=c("vxa"="Vaccines adminsitered (US)",
"vxc"="Became fully vaccinated (US)"
),
yLab=c("vxa"="Rolling 21-day mean administered (000)",
"vxc"="Rolling 21-day mean completed (000)"
)
)## Warning: Removed 20 row(s) containing missing values (geom_path).
## Warning: Removed 20 row(s) containing missing values (geom_path).
## # A tibble: 3,936 x 8
## date state vxc vxa vxc_isPeak vxa_isPeak vxc_isValley vxa_isValley
## <date> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl>
## 1 2020-12-01 CA NA NA FALSE FALSE FALSE FALSE
## 2 2020-12-01 FL NA NA FALSE FALSE FALSE FALSE
## 3 2020-12-01 GA NA NA FALSE FALSE FALSE FALSE
## 4 2020-12-01 IL NA NA FALSE FALSE FALSE FALSE
## 5 2020-12-01 MI NA NA FALSE FALSE FALSE FALSE
## 6 2020-12-01 NC NA NA FALSE FALSE FALSE FALSE
## 7 2020-12-01 NJ NA NA FALSE FALSE FALSE FALSE
## 8 2020-12-01 NY NA NA FALSE FALSE FALSE FALSE
## 9 2020-12-01 OH NA NA FALSE FALSE FALSE FALSE
## 10 2020-12-01 PA NA NA FALSE FALSE FALSE FALSE
## # ... with 3,926 more rowsThe latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211104.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211104.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211104.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_211006")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_211006")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_211006")$dfRaw$vax
)
cdc_daily_211104 <- readRunCDCDaily(thruLabel="Nov 03, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-02-19 tot_deaths 0 246 246 2.00000000
## 2 2020-02-20 tot_deaths 0 246 246 2.00000000
## 3 2020-02-21 tot_deaths 0 246 246 2.00000000
## 4 2020-02-22 tot_deaths 0 246 246 2.00000000
## 5 2020-02-23 tot_deaths 0 246 246 2.00000000
## 6 2020-02-24 tot_deaths 0 246 246 2.00000000
## 7 2020-02-25 tot_deaths 0 246 246 2.00000000
## 8 2020-02-26 tot_deaths 0 246 246 2.00000000
## 9 2020-02-08 tot_deaths 0 245 245 2.00000000
## 10 2020-02-09 tot_deaths 0 245 245 2.00000000
## 11 2020-02-10 tot_deaths 0 245 245 2.00000000
## 12 2020-02-11 tot_deaths 0 245 245 2.00000000
## 13 2020-02-12 tot_deaths 0 245 245 2.00000000
## 14 2020-02-13 tot_deaths 0 245 245 2.00000000
## 15 2020-02-14 tot_deaths 0 245 245 2.00000000
## 16 2020-02-15 tot_deaths 0 245 245 2.00000000
## 17 2020-02-16 tot_deaths 0 245 245 2.00000000
## 18 2020-02-17 tot_deaths 0 245 245 2.00000000
## 19 2020-02-18 tot_deaths 0 245 245 2.00000000
## 20 2020-02-03 tot_deaths 0 244 244 2.00000000
## 21 2020-02-04 tot_deaths 0 244 244 2.00000000
## 22 2020-02-05 tot_deaths 0 244 244 2.00000000
## 23 2020-02-06 tot_deaths 0 244 244 2.00000000
## 24 2020-02-07 tot_deaths 0 244 244 2.00000000
## 25 2020-02-27 tot_deaths 1 247 246 1.98387097
## 26 2020-02-28 tot_deaths 1 247 246 1.98387097
## 27 2020-02-29 tot_deaths 2 248 246 1.96800000
## 28 2020-03-01 tot_deaths 2 248 246 1.96800000
## 29 2020-03-02 tot_deaths 8 254 246 1.87786260
## 30 2020-03-03 tot_deaths 11 257 246 1.83582090
## 31 2020-03-04 tot_deaths 13 259 246 1.80882353
## 32 2020-03-05 tot_deaths 16 262 246 1.76978417
## 33 2020-03-06 tot_deaths 19 266 247 1.73333333
## 34 2020-03-07 tot_deaths 24 271 247 1.67457627
## 35 2020-03-08 tot_deaths 28 276 248 1.63157895
## 36 2020-03-09 tot_deaths 32 280 248 1.58974359
## 37 2020-03-10 tot_deaths 39 286 247 1.52000000
## 38 2020-03-11 tot_deaths 51 300 249 1.41880342
## 39 2020-03-12 tot_deaths 58 307 249 1.36438356
## 40 2020-03-13 tot_deaths 69 318 249 1.28682171
## 41 2020-03-14 tot_deaths 82 332 250 1.20772947
## 42 2020-03-15 tot_deaths 102 352 250 1.10132159
## 43 2020-03-16 tot_deaths 123 373 250 1.00806452
## 44 2020-03-17 tot_deaths 152 405 253 0.90843806
## 45 2020-03-18 tot_deaths 221 476 255 0.73170732
## 46 2020-03-19 tot_deaths 287 541 254 0.61352657
## 47 2020-03-20 tot_deaths 392 643 251 0.48502415
## 48 2020-03-21 tot_deaths 506 758 252 0.39873418
## 49 2020-03-22 tot_deaths 637 893 256 0.33464052
## 50 2020-03-23 tot_deaths 806 1064 258 0.27593583
## 51 2020-03-24 tot_deaths 1026 1298 272 0.23407917
## 52 2020-03-25 tot_deaths 1332 1610 278 0.18898708
## 53 2020-03-26 tot_deaths 1682 1963 281 0.15418381
## 54 2020-03-27 tot_deaths 2178 2467 289 0.12443488
## 55 2020-03-28 tot_deaths 2729 3011 282 0.09825784
## 56 2020-03-30 tot_deaths 4074 4377 303 0.07170749
## 57 2020-03-31 tot_deaths 5071 5387 316 0.06043221
## 58 2020-04-01 tot_deaths 6202 6527 325 0.05106450
## 59 2020-02-09 tot_cases 26 579 553 1.82809917
## 60 2020-02-06 tot_cases 24 533 509 1.82764811
## 61 2020-02-05 tot_cases 24 531 507 1.82702703
## 62 2020-02-11 tot_cases 27 597 570 1.82692308
## 63 2020-02-04 tot_cases 24 526 502 1.82545455
## 64 2020-02-10 tot_cases 27 588 561 1.82439024
## 65 2020-02-03 tot_cases 24 519 495 1.82320442
## 66 2020-02-12 tot_cases 28 605 577 1.82306477
## 67 2020-02-07 tot_cases 25 538 513 1.82238011
## 68 2020-02-08 tot_cases 26 546 520 1.81818182
## 69 2020-02-13 tot_cases 30 611 581 1.81279251
## 70 2020-02-14 tot_cases 31 617 586 1.80864198
## 71 2020-02-16 tot_cases 33 635 602 1.80239521
## 72 2020-02-15 tot_cases 33 622 589 1.79847328
## 73 2020-02-17 tot_cases 39 653 614 1.77456647
## 74 2020-02-18 tot_cases 44 660 616 1.75000000
## 75 2020-02-19 tot_cases 47 677 630 1.74033149
## 76 2020-02-20 tot_cases 49 691 642 1.73513514
## 77 2020-02-21 tot_cases 56 708 652 1.70680628
## 78 2020-02-23 tot_cases 63 757 694 1.69268293
## 79 2020-02-22 tot_cases 62 734 672 1.68844221
## 80 2020-02-24 tot_cases 71 776 705 1.66469894
## 81 2020-02-25 tot_cases 76 800 724 1.65296804
## 82 2020-02-26 tot_cases 86 844 758 1.63010753
## 83 2020-02-27 tot_cases 97 879 782 1.60245902
## 84 2020-02-28 tot_cases 105 930 825 1.59420290
## 85 2020-02-29 tot_cases 117 966 849 1.56786704
## 86 2020-03-01 tot_cases 149 1052 903 1.50374688
## 87 2020-03-02 tot_cases 192 1130 938 1.41906203
## 88 2020-03-03 tot_cases 266 1301 1035 1.32099553
## 89 2020-03-04 tot_cases 343 1444 1101 1.23223279
## 90 2020-03-05 tot_cases 430 1609 1179 1.15644924
## 91 2020-03-06 tot_cases 562 1827 1265 1.05902051
## 92 2020-03-07 tot_cases 734 2089 1355 0.95997166
## 93 2020-03-08 tot_cases 959 2444 1485 0.87275933
## 94 2020-03-09 tot_cases 1411 3040 1629 0.73197034
## 95 2020-03-10 tot_cases 1931 3716 1785 0.63219409
## 96 2020-03-11 tot_cases 2477 4569 2092 0.59381209
## 97 2020-03-12 tot_cases 3289 5713 2424 0.53854699
## 98 2020-03-13 tot_cases 4284 7079 2795 0.49194755
## 99 2020-03-14 tot_cases 5675 8933 3258 0.44605696
## 100 2020-03-15 tot_cases 7959 11743 3784 0.38412344
## 101 2020-03-16 tot_cases 10452 14682 4230 0.33659585
## 102 2020-03-17 tot_cases 13979 18729 4750 0.29044882
## 103 2020-03-18 tot_cases 19249 24821 5572 0.25287043
## 104 2020-03-19 tot_cases 25808 32036 6228 0.21533781
## 105 2020-03-20 tot_cases 33664 40317 6653 0.17985699
## 106 2020-03-21 tot_cases 43174 50375 7201 0.15395141
## 107 2020-03-22 tot_cases 54116 62013 7897 0.13600393
## 108 2020-03-23 tot_cases 65096 73411 8315 0.12006613
## 109 2020-03-24 tot_cases 76834 85682 8848 0.10888774
## 110 2020-03-25 tot_cases 91262 100538 9276 0.09672576
## 111 2020-03-26 tot_cases 110290 119932 9642 0.08376263
## 112 2020-03-27 tot_cases 130734 140627 9893 0.07291394
## 113 2020-03-28 tot_cases 151440 161456 10016 0.06402127
## 114 2020-03-30 tot_cases 193754 204591 10837 0.05441012
## 115 2021-06-06 new_deaths 441 230 211 0.62891207
## 116 2021-08-11 new_deaths 579 992 413 0.52577976
## 117 2021-03-22 new_deaths 951 594 357 0.46213592
## 118 2021-02-25 new_deaths 2863 1834 1029 0.43815201
## 119 2021-06-12 new_deaths 197 298 101 0.40808081
## 120 2021-07-06 new_deaths 294 206 88 0.35200000
## 121 2021-10-03 new_deaths 940 687 253 0.31100184
## 122 2021-07-12 new_deaths 306 229 77 0.28785047
## 123 2021-05-28 new_deaths 687 524 163 0.26919901
## 124 2021-07-01 new_deaths 288 220 68 0.26771654
## 125 2021-09-27 new_deaths 1471 1127 344 0.26481909
## 126 2021-03-28 new_deaths 616 473 143 0.26262626
## 127 2021-01-04 new_deaths 2247 2914 667 0.25847704
## 128 2021-03-25 new_deaths 990 767 223 0.25384178
## 129 2021-03-10 new_deaths 1562 1218 344 0.24748201
## 130 2021-05-02 new_deaths 522 409 113 0.24274973
## 131 2020-09-30 new_deaths 678 537 141 0.23209877
## 132 2021-09-26 new_deaths 1026 817 209 0.22680412
## 133 2021-03-24 new_deaths 1076 858 218 0.22543950
## 134 2021-05-01 new_deaths 636 508 128 0.22377622
## 135 2021-06-24 new_deaths 321 257 64 0.22145329
## 136 2021-06-25 new_deaths 397 319 78 0.21787709
## 137 2021-06-26 new_deaths 238 192 46 0.21395349
## 138 2021-03-27 new_deaths 836 676 160 0.21164021
## 139 2021-05-08 new_deaths 629 509 120 0.21089631
## 140 2021-03-04 new_deaths 1523 1235 288 0.20884699
## 141 2021-04-10 new_deaths 763 619 144 0.20839363
## 142 2020-12-28 new_deaths 2354 2899 545 0.20750048
## 143 2021-03-07 new_deaths 918 748 170 0.20408163
## 144 2021-09-24 new_deaths 2362 1925 437 0.20387217
## 145 2020-08-17 new_deaths 681 834 153 0.20198020
## 146 2021-03-26 new_deaths 1032 845 187 0.19925413
## 147 2021-03-17 new_deaths 1023 838 185 0.19881784
## 148 2020-08-31 new_deaths 564 688 124 0.19808307
## 149 2020-12-26 new_deaths 1839 2238 399 0.19573216
## 150 2020-09-07 new_deaths 458 557 99 0.19507389
## 151 2021-07-08 new_deaths 242 200 42 0.19004525
## 152 2021-02-26 new_deaths 1837 1519 318 0.18951132
## 153 2020-08-24 new_deaths 645 779 134 0.18820225
## 154 2021-06-18 new_deaths 276 229 47 0.18613861
## 155 2020-03-30 new_deaths 654 785 131 0.18207088
## 156 2021-03-13 new_deaths 948 791 157 0.18056354
## 157 2021-06-27 new_deaths 176 147 29 0.17956656
## 158 2020-09-14 new_deaths 451 539 88 0.17777778
## 159 2021-10-04 new_deaths 1233 1032 201 0.17748344
## 160 2021-03-18 new_deaths 847 709 138 0.17737789
## 161 2021-01-03 new_deaths 2149 2559 410 0.17417162
## 162 2021-04-02 new_deaths 813 683 130 0.17379679
## 163 2020-03-29 new_deaths 691 581 110 0.17295597
## 164 2020-12-27 new_deaths 2022 2404 382 0.17261636
## 165 2021-06-05 new_deaths 380 320 60 0.17142857
## 166 2021-04-01 new_deaths 818 689 129 0.17120106
## 167 2021-08-02 new_deaths 666 561 105 0.17114914
## 168 2021-10-02 new_deaths 1183 997 186 0.17064220
## 169 2021-07-16 new_deaths 359 303 56 0.16918429
## 170 2021-03-11 new_deaths 1302 1101 201 0.16729089
## 171 2021-01-19 new_deaths 2575 3042 467 0.16628093
## 172 2021-03-12 new_deaths 1266 1072 194 0.16595381
## 173 2021-02-28 new_deaths 1071 908 163 0.16472966
## 174 2021-09-25 new_deaths 1316 1116 200 0.16447368
## 175 2021-09-22 new_deaths 2123 1802 321 0.16356688
## 176 2021-09-23 new_deaths 2035 1730 305 0.16201859
## 177 2020-07-05 new_deaths 486 571 85 0.16083254
## 178 2021-06-11 new_deaths 402 343 59 0.15838926
## 179 2021-04-04 new_deaths 539 461 78 0.15600000
## 180 2021-02-27 new_deaths 1539 1318 221 0.15470774
## 181 2021-04-08 new_deaths 838 718 120 0.15424165
## 182 2020-09-05 new_deaths 783 671 112 0.15405777
## 183 2021-04-09 new_deaths 822 705 117 0.15324165
## 184 2021-06-20 new_deaths 227 195 32 0.15165877
## 185 2020-08-30 new_deaths 742 638 104 0.15072464
## 186 2021-03-05 new_deaths 1910 1643 267 0.15029552
## 187 2021-09-19 new_deaths 1203 1036 167 0.14917374
## 188 2020-10-02 new_deaths 896 772 124 0.14868106
## 189 2021-07-09 new_deaths 314 271 43 0.14700855
## 190 2021-03-19 new_deaths 1187 1025 162 0.14647378
## 191 2021-04-03 new_deaths 758 655 103 0.14578910
## 192 2020-09-28 new_deaths 449 518 69 0.14270941
## 193 2021-09-21 new_deaths 2217 1930 287 0.13841331
## 194 2020-07-13 new_deaths 757 867 110 0.13546798
## 195 2020-12-29 new_deaths 3313 3792 479 0.13483462
## 196 2021-04-15 new_deaths 787 688 99 0.13423729
## 197 2021-06-04 new_deaths 597 522 75 0.13404826
## 198 2021-05-29 new_deaths 307 351 44 0.13373860
## 199 2021-01-18 new_deaths 2405 2748 343 0.13312633
## 200 2021-07-11 new_deaths 127 145 18 0.13235294
## 201 2020-12-21 new_deaths 2346 2676 330 0.13142174
## 202 2021-03-06 new_deaths 1229 1078 151 0.13090594
## 203 2021-01-25 new_deaths 2214 2524 310 0.13085690
## 204 2021-01-12 new_deaths 3604 4107 503 0.13046297
## 205 2021-04-11 new_deaths 489 430 59 0.12840044
## 206 2020-07-27 new_deaths 981 1114 133 0.12696897
## 207 2021-02-20 new_deaths 1855 1634 221 0.12668386
## 208 2021-03-03 new_deaths 1546 1362 184 0.12654746
## 209 2021-09-15 new_deaths 2264 1996 268 0.12582160
## 210 2021-04-22 new_deaths 773 682 91 0.12508591
## 211 2021-02-05 new_deaths 3245 2863 382 0.12508186
## 212 2021-07-18 new_deaths 150 170 20 0.12500000
## 213 2021-07-25 new_deaths 249 282 33 0.12429379
## 214 2021-03-31 new_deaths 958 846 112 0.12416851
## 215 2021-09-28 new_deaths 1986 1754 232 0.12406417
## 216 2020-07-20 new_deaths 910 1029 119 0.12274368
## 217 2021-02-16 new_deaths 1413 1595 182 0.12101064
## 218 2021-10-01 new_deaths 2130 1889 241 0.11993033
## 219 2021-02-14 new_deaths 1511 1341 170 0.11921459
## 220 2021-05-05 new_deaths 712 632 80 0.11904762
## 221 2020-08-04 new_deaths 1081 1217 136 0.11836379
## 222 2020-08-23 new_deaths 851 756 95 0.11823273
## 223 2020-12-22 new_deaths 2901 3264 363 0.11776156
## 224 2021-07-17 new_deaths 176 198 22 0.11764706
## 225 2021-04-30 new_deaths 787 701 86 0.11559140
## 226 2020-07-28 new_deaths 1198 1343 145 0.11412830
## 227 2020-06-28 new_deaths 430 482 52 0.11403509
## 228 2020-08-18 new_deaths 922 1033 111 0.11355499
## 229 2021-04-14 new_deaths 784 700 84 0.11320755
## 230 2021-09-17 new_deaths 2208 1973 235 0.11241330
## 231 2020-06-30 new_deaths 630 705 75 0.11235955
## 232 2021-07-05 new_deaths 126 141 15 0.11235955
## 233 2020-09-23 new_deaths 908 812 96 0.11162791
## 234 2020-07-21 new_deaths 1194 1335 141 0.11150652
## 235 2021-09-11 new_deaths 1655 1481 174 0.11096939
## 236 2021-07-03 new_deaths 145 162 17 0.11074919
## 237 2021-04-17 new_deaths 642 576 66 0.10837438
## 238 2021-09-16 new_deaths 2039 1832 207 0.10694911
## 239 2021-05-07 new_deaths 800 719 81 0.10664911
## 240 2020-06-22 new_deaths 524 583 59 0.10659440
## 241 2021-01-11 new_deaths 2727 3032 305 0.10592117
## 242 2021-04-18 new_deaths 517 465 52 0.10590631
## 243 2020-08-11 new_deaths 1109 1233 124 0.10589240
## 244 2021-04-29 new_deaths 756 680 76 0.10584958
## 245 2020-09-02 new_deaths 878 791 87 0.10425404
## 246 2021-01-05 new_deaths 3384 3755 371 0.10393613
## 247 2020-09-19 new_deaths 659 594 65 0.10375100
## 248 2021-07-24 new_deaths 275 305 30 0.10344828
## 249 2021-05-16 new_deaths 462 417 45 0.10238908
## 250 2020-05-13 new_deaths 1488 1648 160 0.10204082
## 251 2020-12-20 new_deaths 2164 2396 232 0.10175439
## 252 2020-07-14 new_deaths 925 1024 99 0.10159056
## 253 2020-05-14 new_deaths 1967 1779 188 0.10037373
## 254 2021-02-18 new_deaths 2208 1997 211 0.10035672
## 255 2021-02-19 new_deaths 2346 2124 222 0.09932886
## 256 2020-09-01 new_deaths 869 959 90 0.09846827
## 257 2021-02-06 new_deaths 2570 2330 240 0.09795918
## 258 2021-07-02 new_deaths 300 272 28 0.09790210
## 259 2021-02-21 new_deaths 1448 1313 135 0.09779066
## 260 2021-07-19 new_deaths 291 264 27 0.09729730
## 261 2021-04-07 new_deaths 809 734 75 0.09721322
## 262 2020-07-06 new_deaths 650 716 66 0.09663250
## 263 2021-09-18 new_deaths 1495 1358 137 0.09603926
## 264 2021-09-30 new_deaths 1893 1721 172 0.09518539
## 265 2020-12-14 new_deaths 2150 2364 214 0.09481613
## 266 2021-05-06 new_deaths 669 609 60 0.09389671
## 267 2021-08-30 new_deaths 1210 1329 119 0.09373769
## 268 2021-02-02 new_deaths 2494 2739 245 0.09363654
## 269 2020-10-22 new_deaths 942 858 84 0.09333333
## 270 2020-12-15 new_deaths 2734 3000 266 0.09277991
## 271 2020-06-15 new_deaths 548 601 53 0.09225413
## 272 2020-08-28 new_deaths 1046 954 92 0.09200000
## 273 2021-02-04 new_deaths 3027 2764 263 0.09083060
## 274 2021-06-14 new_deaths 202 221 19 0.08983452
## 275 2020-10-08 new_deaths 745 681 64 0.08976157
## 276 2021-09-05 new_deaths 1295 1184 111 0.08955224
## 277 2021-06-23 new_deaths 340 311 29 0.08909370
## 278 2021-02-24 new_deaths 1985 1816 169 0.08892397
## 279 2021-07-26 new_deaths 423 387 36 0.08888889
## 280 2020-09-21 new_deaths 602 657 55 0.08737093
## 281 2021-05-09 new_deaths 420 385 35 0.08695652
## 282 2021-06-19 new_deaths 228 209 19 0.08695652
## 283 2021-09-10 new_deaths 2274 2087 187 0.08576015
## 284 2020-12-24 new_deaths 2979 3243 264 0.08486017
## 285 2020-10-12 new_deaths 542 590 48 0.08480565
## 286 2021-01-26 new_deaths 3122 3398 276 0.08466258
## 287 2020-08-10 new_deaths 849 924 75 0.08460237
## 288 2020-09-03 new_deaths 954 877 77 0.08410705
## 289 2021-04-28 new_deaths 831 764 67 0.08401254
## 290 2021-02-12 new_deaths 2382 2192 190 0.08307827
## 291 2021-09-29 new_deaths 2201 2030 171 0.08083195
## 292 2020-09-04 new_deaths 915 844 71 0.08072769
## 293 2020-06-23 new_deaths 718 778 60 0.08021390
## 294 2020-08-03 new_deaths 942 1020 78 0.07951070
## 295 2021-04-23 new_deaths 852 787 65 0.07931666
## 296 2020-09-24 new_deaths 751 694 57 0.07889273
## 297 2020-07-04 new_deaths 529 572 43 0.07811081
## 298 2021-06-03 new_deaths 466 431 35 0.07803790
## 299 2021-01-30 new_deaths 2783 2574 209 0.07802875
## 300 2021-02-11 new_deaths 3085 2854 231 0.07779087
## 301 2020-08-25 new_deaths 903 976 73 0.07770090
## 302 2020-09-12 new_deaths 647 599 48 0.07704655
## 303 2021-08-15 new_deaths 724 782 58 0.07702523
## 304 2021-02-09 new_deaths 2323 2509 186 0.07698675
## 305 2021-01-28 new_deaths 3392 3141 251 0.07684066
## 306 2020-10-15 new_deaths 748 693 55 0.07633588
## 307 2020-09-16 new_deaths 1063 985 78 0.07617188
## 308 2020-05-25 new_deaths 698 753 55 0.07580979
## 309 2021-09-09 new_deaths 2169 2011 158 0.07559809
## 310 2020-06-16 new_deaths 663 715 52 0.07547170
## 311 2021-05-22 new_deaths 427 396 31 0.07533414
## 312 2020-10-18 new_deaths 768 713 55 0.07427414
## 313 2021-01-02 new_deaths 2457 2646 189 0.07407407
## 314 2020-09-22 new_deaths 663 714 51 0.07407407
## 315 2021-07-10 new_deaths 143 154 11 0.07407407
## 316 2021-08-23 new_deaths 1132 1219 87 0.07401106
## 317 2020-10-03 new_deaths 560 603 43 0.07394669
## 318 2020-10-06 new_deaths 640 689 49 0.07373965
## 319 2021-04-21 new_deaths 750 697 53 0.07325501
## 320 2020-11-30 new_deaths 1721 1851 130 0.07278835
## 321 2021-01-10 new_deaths 2614 2810 196 0.07227139
## 322 2020-12-07 new_deaths 2156 2317 161 0.07198748
## 323 2020-10-28 new_deaths 1083 1008 75 0.07173601
## 324 2021-02-10 new_deaths 2751 2561 190 0.07153614
## 325 2020-12-08 new_deaths 2564 2752 188 0.07072987
## 326 2021-07-23 new_deaths 416 388 28 0.06965174
## 327 2021-05-12 new_deaths 702 655 47 0.06927045
## 328 2021-05-31 new_deaths 252 270 18 0.06896552
## 329 2020-05-26 new_deaths 843 903 60 0.06872852
## 330 2021-05-21 new_deaths 634 592 42 0.06851550
## 331 2020-08-12 new_deaths 1344 1255 89 0.06848788
## 332 2021-05-23 new_deaths 288 269 19 0.06822262
## 333 2020-07-10 new_deaths 940 878 62 0.06820682
## 334 2020-11-29 new_deaths 1397 1495 98 0.06777317
## 335 2020-06-29 new_deaths 502 537 35 0.06737247
## 336 2020-10-01 new_deaths 754 705 49 0.06716929
## 337 2021-08-16 new_deaths 835 893 58 0.06712963
## 338 2020-07-09 new_deaths 944 883 61 0.06677614
## 339 2020-08-09 new_deaths 916 857 59 0.06655386
## 340 2021-07-31 new_deaths 395 422 27 0.06609547
## 341 2020-09-06 new_deaths 706 661 45 0.06583760
## 342 2020-08-22 new_deaths 959 898 61 0.06569736
## 343 2021-03-14 new_deaths 678 635 43 0.06549886
## 344 2020-09-13 new_deaths 761 713 48 0.06512890
## 345 2020-09-18 new_deaths 825 773 52 0.06508135
## 346 2020-10-11 new_deaths 603 565 38 0.06506849
## 347 2020-12-12 new_deaths 2413 2572 159 0.06379137
## 348 2020-07-30 new_deaths 1448 1359 89 0.06341290
## 349 2021-08-17 new_deaths 1169 1245 76 0.06296603
## 350 2021-02-13 new_deaths 1988 1867 121 0.06277562
## 351 2021-09-01 new_deaths 1989 1868 121 0.06274306
## 352 2020-11-01 new_deaths 748 703 45 0.06202619
## 353 2021-01-20 new_deaths 3789 4031 242 0.06189258
## 354 2020-03-24 new_deaths 220 234 14 0.06167401
## 355 2021-06-17 new_deaths 285 268 17 0.06148282
## 356 2020-10-13 new_deaths 697 741 44 0.06119611
## 357 2021-07-04 new_deaths 127 135 8 0.06106870
## 358 2021-08-09 new_deaths 747 793 46 0.05974026
## 359 2020-11-16 new_deaths 1138 1208 70 0.05967604
## 360 2020-12-23 new_deaths 3211 3406 195 0.05893910
## 361 2021-06-10 new_deaths 402 379 23 0.05889885
## 362 2021-05-26 new_deaths 527 497 30 0.05859375
## 363 2020-09-29 new_deaths 813 862 49 0.05850746
## 364 2020-05-18 new_deaths 982 1041 59 0.05832921
## 365 2021-02-01 new_deaths 2083 2208 125 0.05826148
## 366 2021-03-09 new_deaths 1004 1064 60 0.05802708
## 367 2021-05-19 new_deaths 604 570 34 0.05792164
## 368 2021-05-20 new_deaths 534 504 30 0.05780347
## 369 2021-03-01 new_deaths 1229 1160 69 0.05776476
## 370 2020-06-08 new_deaths 795 842 47 0.05742211
## 371 2021-02-23 new_deaths 1647 1744 97 0.05721026
## 372 2021-01-06 new_deaths 3893 4121 228 0.05690042
## 373 2020-10-23 new_deaths 942 890 52 0.05676856
## 374 2020-08-14 new_deaths 1070 1011 59 0.05670351
## 375 2021-03-20 new_deaths 690 730 40 0.05633803
## 376 2021-04-05 new_deaths 384 406 22 0.05569620
## 377 2020-06-25 new_deaths 711 673 38 0.05491329
## 378 2020-09-26 new_deaths 674 638 36 0.05487805
## 379 2021-01-27 new_deaths 3655 3460 195 0.05481377
## 380 2021-04-25 new_deaths 450 426 24 0.05479452
## 381 2020-10-26 new_deaths 729 770 41 0.05470314
## 382 2021-07-15 new_deaths 321 304 17 0.05440000
## 383 2020-09-17 new_deaths 737 698 39 0.05435540
## 384 2020-11-03 new_deaths 1074 1134 60 0.05434783
## 385 2021-06-28 new_deaths 197 208 11 0.05432099
## 386 2021-04-06 new_deaths 686 724 38 0.05390071
## 387 2020-06-19 new_deaths 732 694 38 0.05329593
## 388 2021-08-10 new_deaths 943 993 50 0.05165289
## 389 2020-05-11 new_deaths 1096 1154 58 0.05155556
## 390 2021-08-24 new_deaths 1562 1644 82 0.05115409
## 391 2020-09-25 new_deaths 742 705 37 0.05114029
## 392 2021-04-16 new_deaths 803 763 40 0.05108557
## 393 2020-12-25 new_deaths 2367 2491 124 0.05104981
## 394 2021-05-14 new_deaths 684 650 34 0.05097451
## 395 2021-04-27 new_deaths 520 547 27 0.05060918
## 396 2020-11-02 new_deaths 813 855 42 0.05035971
## 397 2020-08-19 new_deaths 1248 1187 61 0.05010267
## 398 2020-02-09 new_cases 0 33 33 2.00000000
## 399 2020-02-16 new_cases 0 13 13 2.00000000
## 400 2020-02-11 new_cases 0 9 9 2.00000000
## 401 2020-02-04 new_cases 0 7 7 2.00000000
## 402 2020-02-23 new_cases 1 23 22 1.83333333
## 403 2020-02-10 new_cases 1 9 8 1.60000000
## 404 2020-02-08 new_cases 1 8 7 1.55555556
## 405 2020-02-12 new_cases 1 8 7 1.55555556
## 406 2020-02-20 new_cases 2 14 12 1.50000000
## 407 2020-02-03 new_cases 4 26 22 1.46666667
## 408 2020-02-28 new_cases 8 51 43 1.45762712
## 409 2020-02-19 new_cases 3 17 14 1.40000000
## 410 2020-02-25 new_cases 5 24 19 1.31034483
## 411 2020-02-26 new_cases 10 44 34 1.25925926
## 412 2020-02-22 new_cases 6 26 20 1.25000000
## 413 2020-02-27 new_cases 11 35 24 1.04347826
## 414 2020-02-29 new_cases 12 36 24 1.00000000
## 415 2020-02-17 new_cases 6 18 12 1.00000000
## 416 2020-03-01 new_cases 32 86 54 0.91525424
## 417 2020-02-21 new_cases 7 17 10 0.83333333
## 418 2020-02-24 new_cases 8 19 11 0.81481481
## 419 2020-03-03 new_cases 74 171 97 0.79183673
## 420 2020-03-05 new_cases 87 165 78 0.61904762
## 421 2020-03-04 new_cases 77 143 66 0.60000000
## 422 2020-03-02 new_cases 43 78 35 0.57851240
## 423 2020-03-06 new_cases 132 218 86 0.49142857
## 424 2020-03-08 new_cases 225 355 130 0.44827586
## 425 2020-03-11 new_cases 546 853 307 0.43888492
## 426 2021-06-30 new_cases 11473 17894 6421 0.43729356
## 427 2020-03-07 new_cases 172 262 90 0.41474654
## 428 2020-03-12 new_cases 812 1144 332 0.33946830
## 429 2020-03-13 new_cases 995 1366 371 0.31427361
## 430 2020-03-30 new_cases 15993 21742 5749 0.30470386
## 431 2020-03-14 new_cases 1391 1854 463 0.28536210
## 432 2020-05-13 new_cases 17768 23600 5832 0.28195707
## 433 2020-03-09 new_cases 452 596 144 0.27480916
## 434 2020-12-27 new_cases 156008 119489 36519 0.26511359
## 435 2021-08-02 new_cases 106092 81546 24546 0.26163144
## 436 2020-03-10 new_cases 520 676 156 0.26086957
## 437 2021-10-04 new_cases 90021 71127 18894 0.23449252
## 438 2021-07-26 new_cases 72995 58108 14887 0.22710388
## 439 2021-09-27 new_cases 107376 86322 21054 0.21738996
## 440 2021-07-19 new_cases 46704 37654 9050 0.21456175
## 441 2021-07-06 new_cases 20945 16896 4049 0.21400069
## 442 2021-07-11 new_cases 16761 20648 3887 0.20781095
## 443 2020-03-29 new_cases 26321 21393 4928 0.20656411
## 444 2020-03-15 new_cases 2284 2810 526 0.20651747
## 445 2021-07-12 new_cases 30021 24442 5579 0.20487303
## 446 2021-07-18 new_cases 28571 34924 6353 0.20011024
## 447 2021-07-04 new_cases 10306 12495 2189 0.19200912
## 448 2020-12-31 new_cases 231161 278813 47652 0.18688012
## 449 2021-07-17 new_cases 31623 37862 6239 0.17957833
## 450 2021-07-25 new_cases 43489 51945 8456 0.17721148
## 451 2021-07-05 new_cases 9336 11149 1813 0.17700757
## 452 2021-05-28 new_cases 24154 20325 3829 0.17217114
## 453 2021-07-10 new_cases 20528 24383 3855 0.17167286
## 454 2021-08-01 new_cases 63068 74674 11606 0.16851795
## 455 2020-03-16 new_cases 2493 2939 446 0.16421208
## 456 2021-07-03 new_cases 12986 15304 2318 0.16387416
## 457 2020-08-11 new_cases 61541 52255 9286 0.16320433
## 458 2020-08-15 new_cases 51474 44116 7358 0.15394916
## 459 2021-07-24 new_cases 52916 61584 8668 0.15140611
## 460 2020-05-14 new_cases 34697 29965 4732 0.14636108
## 461 2020-03-18 new_cases 5270 6092 822 0.14469284
## 462 2021-07-31 new_cases 80911 93240 12329 0.14158977
## 463 2020-03-17 new_cases 3527 4047 520 0.13731186
## 464 2021-01-03 new_cases 204160 179121 25039 0.13065610
## 465 2020-06-28 new_cases 37209 42343 5134 0.12907281
## 466 2020-07-06 new_cases 52042 45921 6121 0.12496555
## 467 2021-01-06 new_cases 262230 295251 33021 0.11846502
## 468 2020-12-30 new_cases 246663 276152 29489 0.11280855
## 469 2021-01-04 new_cases 176947 197846 20899 0.11152289
## 470 2020-06-17 new_cases 26594 29563 2969 0.10573927
## 471 2021-01-25 new_cases 134678 121822 12856 0.10024172
## 472 2020-06-26 new_cases 47543 52516 4973 0.09940135
## 473 2020-03-19 new_cases 6559 7215 656 0.09525192
## 474 2021-07-08 new_cases 28170 30696 2526 0.08582204
## 475 2020-06-25 new_cases 48479 52825 4346 0.08580115
## 476 2020-06-21 new_cases 27068 29469 2401 0.08493553
## 477 2021-06-27 new_cases 10461 9619 842 0.08386454
## 478 2020-08-12 new_cases 56265 51773 4492 0.08315593
## 479 2020-12-24 new_cases 201020 218257 17237 0.08222249
## 480 2020-07-09 new_cases 65119 70660 5541 0.08161792
## 481 2020-07-04 new_cases 49004 53159 4155 0.08134060
## 482 2020-06-20 new_cases 33948 36812 2864 0.08094969
## 483 2020-08-10 new_cases 44083 40663 3420 0.08071177
## 484 2021-09-26 new_cases 76428 82807 6379 0.08012058
## 485 2020-06-04 new_cases 21524 23309 1785 0.07962884
## 486 2020-06-18 new_cases 29829 32210 2381 0.07675817
## 487 2021-01-12 new_cases 226568 210291 16277 0.07451832
## 488 2020-06-27 new_cases 44751 48211 3460 0.07443902
## 489 2020-12-03 new_cases 224021 241314 17293 0.07432495
## 490 2021-01-02 new_cases 218437 202798 15639 0.07425309
## 491 2021-01-07 new_cases 267976 288619 20643 0.07417602
## 492 2021-01-17 new_cases 175155 163046 12109 0.07160830
## 493 2020-08-17 new_cases 40739 37962 2777 0.07057089
## 494 2021-07-07 new_cases 23008 24667 1659 0.06959622
## 495 2020-06-19 new_cases 33683 36109 2426 0.06952086
## 496 2020-08-23 new_cases 34825 32526 2299 0.06826922
## 497 2021-09-25 new_cases 91924 98375 6451 0.06779857
## 498 2021-01-18 new_cases 150824 140938 9886 0.06776756
## 499 2020-08-16 new_cases 39738 37134 2604 0.06774899
## 500 2021-07-14 new_cases 38514 41202 2688 0.06743941
## 501 2020-12-13 new_cases 173327 185082 11755 0.06559545
## 502 2020-07-05 new_cases 38831 41444 2613 0.06510121
## 503 2020-06-14 new_cases 19510 20783 1273 0.06318715
## 504 2020-06-10 new_cases 23111 24611 1500 0.06286409
## 505 2020-03-22 new_cases 10942 11638 696 0.06164748
## 506 2021-01-19 new_cases 156712 147342 9370 0.06163379
## 507 2020-12-10 new_cases 211460 224821 13361 0.06124952
## 508 2020-08-30 new_cases 34704 32645 2059 0.06114419
## 509 2020-07-10 new_cases 70260 74675 4415 0.06092386
## 510 2021-02-07 new_cases 87022 81908 5114 0.06054579
## 511 2020-12-02 new_cases 215178 228315 13137 0.05924333
## 512 2021-07-09 new_cases 27667 29325 1658 0.05818360
## 513 2021-07-28 new_cases 90794 96177 5383 0.05758112
## 514 2020-06-03 new_cases 21740 23026 1286 0.05745432
## 515 2020-07-21 new_cases 66698 62999 3699 0.05704064
## 516 2021-07-21 new_cases 62022 65663 3641 0.05703097
## 517 2020-06-24 new_cases 38812 41058 2246 0.05624139
## 518 2020-03-21 new_cases 9510 10058 548 0.05600981
## 519 2020-11-27 new_cases 158281 167382 9101 0.05589213
## 520 2020-05-29 new_cases 23478 24819 1341 0.05553140
## 521 2020-12-04 new_cases 228275 241170 12895 0.05493721
## 522 2021-08-30 new_cases 133221 126144 7077 0.05457174
## 523 2021-01-05 new_cases 261529 247769 13760 0.05403516
## 524 2020-06-12 new_cases 28247 29799 1552 0.05347483
## 525 2020-03-20 new_cases 7856 8281 425 0.05267398
## 526 2021-01-31 new_cases 107679 102181 5498 0.05239684
## 527 2020-12-09 new_cases 221701 233629 11928 0.05239277
## 528 2020-07-03 new_cases 60571 63799 3228 0.05190962
## 529 2020-07-02 new_cases 56724 59745 3021 0.05187646
## 530 2021-01-11 new_cases 201121 190955 10166 0.05185729
## 531 2020-06-07 new_cases 17171 18083 912 0.05173881
## 532 2020-07-12 new_cases 57261 60274 3013 0.05126983
## 533 2021-07-15 new_cases 40729 42871 2142 0.05124402
## 534 2020-06-15 new_cases 20387 21437 1050 0.05021041
## 535 2021-09-01 new_cases 201579 191720 9859 0.05013488
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 CA tot_deaths 18843980 20243442 1399462 0.071606769
## 2 SC tot_deaths 3200530 3267283 66753 0.020641599
## 3 KY tot_deaths 2377709 2364995 12714 0.005361498
## 4 AL tot_deaths 3929590 3909338 20252 0.005167033
## 5 FL tot_deaths 13535198 13485213 49985 0.003699796
## 6 NM tot_deaths 1422780 1419795 2985 0.002100208
## 7 IN tot_deaths 4753969 4748538 5431 0.001143067
## 8 TN tot_deaths 3992595 3988072 4523 0.001133489
## 9 CA tot_cases 1203611071 1258198901 54587830 0.044347720
## 10 KY tot_cases 148749939 148486653 263286 0.001771558
## 11 KY new_deaths 9412 8906 506 0.055246206
## 12 FL new_deaths 58283 55622 2661 0.046723146
## 13 AL new_deaths 15216 14542 674 0.045298743
## 14 NM new_deaths 4938 4823 115 0.023563160
## 15 IN new_deaths 16059 15773 286 0.017969339
## 16 TN new_deaths 15592 15323 269 0.017402555
## 17 SC new_deaths 12711 12828 117 0.009162457
## 18 MS new_deaths 8856 8906 50 0.005629997
## 19 CA new_deaths 69039 68795 244 0.003540491
## 20 PR new_deaths 3181 3173 8 0.002518099
## 21 RI new_deaths 2846 2843 3 0.001054667
## 22 CA new_cases 4569128 4724440 155312 0.033423546
## 23 KY new_cases 708542 700393 8149 0.011567602
## 24 AL new_cases 807657 802977 4680 0.005811376
## 25 TN new_cases 1242274 1238023 4251 0.003427815
## 26 SC new_cases 868898 866245 2653 0.003057961
## 27 PR new_cases 181993 181797 196 0.001077545
##
##
##
## Raw file for cdcDaily:
## Rows: 39,060
## Columns: 15
## $ date <date> 2021-09-01, 2020-07-14, 2021-02-02, 2021-09-19, 2020-0~
## $ state <chr> "ND", "CA", "IL", "DE", "WI", "ND", "GU", "NC", "MI", "~
## $ tot_cases <dbl> 118491, 336447, 1130917, 128253, 25480, 6602, 449, 8753~
## $ conf_cases <dbl> 107475, 336447, 1130917, 117969, 22932, 6602, NA, 76009~
## $ prob_cases <dbl> 11016, 0, 0, 10284, 2548, 0, NA, 115264, NA, 2026, NA, ~
## $ new_cases <dbl> 536, 7285, 2304, 450, 185, 133, 15, 1614, 0, 621, 1379,~
## $ pnew_case <dbl> 66, 0, 0, 36, 11, 0, 0, 450, NA, -11, NA, 0, 0, NA, -30~
## $ tot_deaths <dbl> 1562, 7039, 21336, 1920, 700, 103, 5, 12363, 0, 3285, 4~
## $ conf_death <dbl> NA, 7039, 19306, 1756, 694, NA, NA, 10933, NA, 2524, NA~
## $ prob_death <dbl> NA, 0, 2030, 164, 6, NA, NA, 1430, NA, 761, NA, 0, NA, ~
## $ new_deaths <dbl> 1, 25, 63, 0, 2, 0, 0, 16, 0, 66, 50, 165, 33, 0, 41, 1~
## $ pnew_death <dbl> 0, 0, 16, 0, 0, 0, 0, 2, NA, 8, NA, 0, 0, NA, -10, 0, 0~
## $ created_at <chr> "09/02/2021 01:49:05 PM", "07/16/2020 12:00:00 AM", "02~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Agree", "Not a~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 UT inp 168021 167794 227 0.001351935
## 2 ME hosp_ped 672 722 50 0.071736011
## 3 SC hosp_ped 4707 4918 211 0.043844156
## 4 VT hosp_ped 113 109 4 0.036036036
## 5 AR hosp_ped 7953 7774 179 0.022763401
## 6 WV hosp_ped 3008 3069 61 0.020075695
## 7 NM hosp_ped 3979 3906 73 0.018516170
## 8 NJ hosp_ped 10532 10704 172 0.016198908
## 9 DE hosp_ped 2403 2436 33 0.013639182
## 10 AZ hosp_ped 14960 15159 199 0.013214250
## 11 MA hosp_ped 5669 5731 62 0.010877193
## 12 AL hosp_ped 12322 12189 133 0.010852270
## 13 MS hosp_ped 6395 6333 62 0.009742300
## 14 UT hosp_ped 3909 3876 33 0.008477842
## 15 IN hosp_ped 9883 9966 83 0.008363142
## 16 MO hosp_ped 21187 21343 156 0.007335998
## 17 NC hosp_ped 14766 14672 94 0.006386303
## 18 AK hosp_ped 1059 1064 5 0.004710316
## 19 KY hosp_ped 9043 9085 42 0.004633716
## 20 RI hosp_ped 1705 1699 6 0.003525264
## 21 VA hosp_ped 9082 9114 32 0.003517257
## 22 IA hosp_ped 3553 3541 12 0.003383141
## 23 PA hosp_ped 25525 25444 81 0.003178403
## 24 WA hosp_ped 6319 6300 19 0.003011332
## 25 TN hosp_ped 11881 11849 32 0.002697008
## 26 NH hosp_ped 381 380 1 0.002628121
## 27 OH hosp_ped 40021 39919 102 0.002551914
## 28 CA hosp_ped 40162 40063 99 0.002468059
## 29 ND hosp_ped 1779 1783 4 0.002245929
## 30 WY hosp_ped 467 468 1 0.002139037
## 31 NV hosp_ped 2739 2744 5 0.001823819
## 32 NE hosp_ped 4153 4146 7 0.001686950
## 33 GA hosp_ped 30374 30330 44 0.001449657
## 34 MN hosp_ped 7296 7305 9 0.001232792
## 35 SD hosp_ped 2714 2711 3 0.001105991
## 36 ME hosp_adult 48896 48821 75 0.001535045
## 37 NV hosp_adult 418591 418168 423 0.001011044
##
##
##
## Raw file for cdcHosp:
## Rows: 32,789
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current: Administered_Fed_LTC Administered_Fed_LTC_Residents Administered_Fed_LTC_Staff Administered_Fed_LTC_Unk Administered_Fed_LTC_Dose1 Administered_Fed_LTC_Dose1_Residents Administered_Fed_LTC_Dose1_Staff Administered_Fed_LTC_Dose1_Unk Series_Complete_FedLTC Series_Complete_FedLTC_Residents Series_Complete_FedLTC_Staff Series_Complete_FedLTC_Unknown
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 21,080
## Columns: 69
## $ date <date> 2021-11-03, 2021-11-03, 2021-1~
## $ MMWR_week <dbl> 44, 44, 44, 44, 44, 44, 44, 44,~
## $ state <chr> "DE", "NE", "VA", "NV", "MH", "~
## $ Distributed <dbl> 1740405, 2855740, 14218415, 440~
## $ Distributed_Janssen <dbl> 78800, 130700, 652600, 220200, ~
## $ Distributed_Moderna <dbl> 688300, 1073900, 5282260, 15782~
## $ Distributed_Pfizer <dbl> 962205, 1649940, 8196255, 26021~
## $ Distributed_Unk_Manuf <dbl> 11100, 1200, 87300, 6900, 0, 69~
## $ Dist_Per_100K <dbl> 178730, 147629, 166579, 143095,~
## $ Distributed_Per_100k_12Plus <dbl> 207239, 176563, 194587, 168111,~
## $ Distributed_Per_100k_18Plus <dbl> 225970, 195822, 213020, 184608,~
## $ Distributed_Per_100k_65Plus <dbl> 921307, 913960, 1046310, 888669~
## $ vxa <dbl> 1323750, 2363841, 11897204, 367~
## $ Administered_12Plus <dbl> 1323514, 2363455, 11865686, 367~
## $ Administered_18Plus <dbl> 1240381, 2204590, 11017333, 344~
## $ Administered_65Plus <dbl> 417001, 672166, 2858340, 938683~
## $ Administered_Janssen <dbl> 52330, 81726, 419235, 159195, 2~
## $ Administered_Moderna <dbl> 503446, 858215, 4230753, 127892~
## $ Administered_Pfizer <dbl> 766399, 1418876, 7240126, 22366~
## $ Administered_Unk_Manuf <dbl> 1575, 5024, 7090, 444, 2, 1659,~
## $ Admin_Per_100k <dbl> 135942, 122200, 139385, 119319,~
## $ Admin_Per_100k_12Plus <dbl> 157597, 146126, 162389, 140177,~
## $ Admin_Per_100k_18Plus <dbl> 161048, 151172, 165062, 144348,~
## $ Admin_Per_100k_65Plus <dbl> 220745, 215122, 210341, 189261,~
## $ Recip_Administered <dbl> 1297136, 2371830, 11887736, 364~
## $ Administered_Dose1_Recip <dbl> 676471, 1183497, 6092097, 19595~
## $ Administered_Dose1_Pop_Pct <dbl> 69.5, 61.2, 71.4, 63.6, 41.3, 7~
## $ Administered_Dose1_Recip_12Plus <dbl> 676280, 1183205, 6073694, 19594~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 80.5, 73.2, 83.1, 74.7, 48.3, 8~
## $ Administered_Dose1_Recip_18Plus <dbl> 634229, 1099541, 5631043, 18314~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 82.3, 75.4, 84.4, 76.7, 50.9, 8~
## $ Administered_Dose1_Recip_65Plus <dbl> 193322, 293535, 1333722, 454040~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 99.9, 93.9, 98.1, 91.5, 13.4, 9~
## $ vxc <dbl> 585496, 1090517, 5400472, 16368~
## $ vxcpoppct <dbl> 60.1, 56.4, 63.3, 53.1, 34.7, 7~
## $ Series_Complete_12Plus <dbl> 585442, 1090423, 5388380, 16368~
## $ Series_Complete_12PlusPop_Pct <dbl> 69.7, 67.4, 73.7, 62.4, 40.7, 8~
## $ vxcgte18 <dbl> 549593, 1015791, 4995190, 15392~
## $ vxcgte18pct <dbl> 71.4, 69.7, 74.8, 64.5, 44.5, 8~
## $ vxcgte65 <dbl> 169840, 278310, 1191309, 392777~
## $ vxcgte65pct <dbl> 89.9, 89.1, 87.7, 79.2, 11.7, 9~
## $ Series_Complete_Janssen <dbl> 51594, 80934, 404962, 156119, 2~
## $ Series_Complete_Moderna <dbl> 212978, 391688, 1869775, 564207~
## $ Series_Complete_Pfizer <dbl> 320411, 616557, 3123032, 916524~
## $ Series_Complete_Unk_Manuf <dbl> 513, 1338, 2703, 40, 1, 372, 14~
## $ Series_Complete_Janssen_12Plus <dbl> 51585, 80916, 404867, 156115, 2~
## $ Series_Complete_Moderna_12Plus <dbl> 212967, 391664, 1869605, 564203~
## $ Series_Complete_Pfizer_12Plus <dbl> 320377, 616507, 3111213, 916492~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 513, 1336, 2695, 40, 1, 372, 14~
## $ Series_Complete_Janssen_18Plus <dbl> 51538, 80860, 403425, 156075, 2~
## $ Series_Complete_Moderna_18Plus <dbl> 212855, 391531, 1864225, 564107~
## $ Series_Complete_Pfizer_18Plus <dbl> 284695, 542118, 2725010, 819040~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 505, 1282, 2530, 38, 1, 336, 13~
## $ Series_Complete_Janssen_65Plus <dbl> 9370, 6570, 68922, 24477, 100, ~
## $ Series_Complete_Moderna_65Plus <dbl> 69591, 135209, 557491, 182550, ~
## $ Series_Complete_Pfizer_65Plus <dbl> 90603, 135700, 564052, 185728, ~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 276, 831, 844, 22, 0, 164, 37, ~
## $ Additional_Doses <dbl> 72183, 144543, 637226, 153957, ~
## $ Additional_Doses_Vax_Pct <dbl> 12.3, 13.3, 11.8, 9.4, 0.5, 12.~
## $ Additional_Doses_18Plus <dbl> 72119, 144363, 636427, 153845, ~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 13.1, 14.2, 12.7, 10.0, 0.5, 13~
## $ Additional_Doses_50Plus <dbl> 63811, 116371, 514120, 129623, ~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 19.7, 21.6, 20.3, 16.3, 0.9, 20~
## $ Additional_Doses_65Plus <dbl> 50550, 89179, 369537, 98301, 10~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 29.8, 32.0, 31.0, 25.0, 0.9, 31~
## $ Additional_Doses_Moderna <dbl> 22244, 32403, 200336, 42487, 10~
## $ Additional_Doses_Pfizer <dbl> 49616, 111328, 431133, 110631, ~
## $ Additional_Doses_Janssen <dbl> 316, 736, 5457, 839, 0, 887, 11~
## $ Additional_Doses_Unk_Manuf <dbl> 7, 76, 300, 0, 0, 135, 11, 61, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.21e+10 2.28e+8 4.58e+7 736156 38409
## 2 after 1.20e+10 2.27e+8 4.56e+7 732594 33201
## 3 pctchg 4.37e- 3 4.29e-3 4.61e-3 0.00484 0.136
##
##
## Processed for cdcDaily:
## Rows: 33,201
## Columns: 6
## $ date <date> 2021-09-01, 2020-07-14, 2021-02-02, 2021-09-19, 2020-06-15~
## $ state <chr> "ND", "CA", "IL", "DE", "WI", "ND", "NC", "MI", "CT", "CT",~
## $ tot_cases <dbl> 118491, 336447, 1130917, 128253, 25480, 6602, 875359, 0, 36~
## $ tot_deaths <dbl> 1562, 7039, 21336, 1920, 700, 103, 12363, 0, 3285, 494, 108~
## $ new_cases <dbl> 536, 7285, 2304, 450, 185, 133, 1614, 0, 621, 1379, 7934, 1~
## $ new_deaths <dbl> 1, 25, 63, 0, 2, 0, 16, 0, 66, 50, 165, 33, 0, 41, 19, 15, ~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.53e+7 2.92e+7 648421 32789
## 2 after 3.51e+7 2.90e+7 634350 31524
## 3 pctchg 5.19e-3 5.06e-3 0.0217 0.0386
##
##
## Processed for cdcHosp:
## Rows: 31,524
## Columns: 5
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "ID", "MN", "HI", "NH", "HI", "KS", "KS", "NH", "ME", "NJ",~
## $ inp <dbl> 221, 625, 99, 45, 110, 476, 474, 52, 23, 579, 836, 156, 47,~
## $ hosp_adult <dbl> 219, 609, 99, 44, 108, 472, 454, 52, 23, 563, 423, 143, 47,~
## $ hosp_ped <dbl> 2, 16, 0, 1, 2, 4, 5, 0, 0, 16, 1, 13, 0, 3, 1, 0, 3, 8, 29~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.56e+11 6.82e+10 608084. 1.98e+10 1002949. 6.53e+10 737691.
## 2 after 7.47e+10 3.30e+10 511921. 9.56e+ 9 914256. 3.16e+10 629721.
## 3 pctchg 5.22e- 1 5.16e- 1 0.158 5.16e- 1 0.0884 5.17e- 1 0.146
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 16,575
## Columns: 9
## $ date <date> 2021-11-03, 2021-11-03, 2021-11-03, 2021-11-03, 2021-11-0~
## $ state <chr> "DE", "NE", "VA", "NV", "ME", "OK", "KY", "IL", "WV", "NM"~
## $ vxa <dbl> 1323750, 2363841, 11897204, 3675224, 1998675, 4529434, 495~
## $ vxc <dbl> 585496, 1090517, 5400472, 1636890, 951425, 1987280, 227231~
## $ vxcpoppct <dbl> 60.1, 56.4, 63.3, 53.1, 70.8, 50.2, 50.9, 60.6, 41.1, 62.3~
## $ vxcgte65 <dbl> 169840, 278310, 1191309, 392777, 271435, 523238, 618148, 1~
## $ vxcgte65pct <dbl> 89.9, 89.1, 87.7, 79.2, 95.2, 82.4, 82.4, 86.0, 71.8, 88.5~
## $ vxcgte18 <dbl> 549593, 1015791, 4995190, 1539260, 896801, 1863794, 214494~
## $ vxcgte18pct <dbl> 71.4, 69.7, 74.8, 64.5, 81.9, 62.0, 61.9, 72.2, 49.1, 74.5~
##
## Integrated per capita data file:
## Rows: 33,414
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211104)The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211202.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211202.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211202.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_211104")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_211104")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_211104")$dfRaw$vax
)
cdc_daily_211202 <- readRunCDCDaily(thruLabel="Dec 01, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-01-22 tot_cases 0 7 7 2.00000000
## 2 2020-01-23 tot_cases 1 8 7 1.55555556
## 3 2020-01-24 tot_cases 2 9 7 1.27272727
## 4 2020-01-25 tot_cases 2 9 7 1.27272727
## 5 2020-01-28 tot_cases 3 11 8 1.14285714
## 6 2020-01-29 tot_cases 3 11 8 1.14285714
## 7 2020-01-30 tot_cases 3 11 8 1.14285714
## 8 2020-02-01 tot_cases 5 17 12 1.09090909
## 9 2020-01-26 tot_cases 3 10 7 1.07692308
## 10 2020-01-27 tot_cases 3 10 7 1.07692308
## 11 2020-01-31 tot_cases 4 13 9 1.05882353
## 12 2020-02-02 tot_cases 8 20 12 0.85714286
## 13 2020-02-17 tot_cases 16 39 23 0.83636364
## 14 2020-02-15 tot_cases 14 33 19 0.80851064
## 15 2020-02-16 tot_cases 14 33 19 0.80851064
## 16 2020-02-10 tot_cases 12 27 15 0.76923077
## 17 2020-02-11 tot_cases 12 27 15 0.76923077
## 18 2020-02-14 tot_cases 14 31 17 0.75555556
## 19 2020-02-03 tot_cases 11 24 13 0.74285714
## 20 2020-02-04 tot_cases 11 24 13 0.74285714
## 21 2020-02-05 tot_cases 11 24 13 0.74285714
## 22 2020-02-06 tot_cases 11 24 13 0.74285714
## 23 2020-02-08 tot_cases 12 26 14 0.73684211
## 24 2020-02-09 tot_cases 12 26 14 0.73684211
## 25 2020-02-12 tot_cases 13 28 15 0.73170732
## 26 2020-02-13 tot_cases 14 30 16 0.72727273
## 27 2020-02-18 tot_cases 21 44 23 0.70769231
## 28 2020-02-07 tot_cases 12 25 13 0.70270270
## 29 2020-02-20 tot_cases 25 49 24 0.64864865
## 30 2020-02-19 tot_cases 24 47 23 0.64788732
## 31 2020-02-21 tot_cases 30 56 26 0.60465116
## 32 2020-02-24 tot_cases 40 71 31 0.55855856
## 33 2020-02-23 tot_cases 36 63 27 0.54545455
## 34 2020-02-25 tot_cases 44 76 32 0.53333333
## 35 2020-02-22 tot_cases 36 62 26 0.53061224
## 36 2020-02-28 tot_cases 61 105 44 0.53012048
## 37 2020-02-27 tot_cases 57 97 40 0.51948052
## 38 2020-03-01 tot_cases 88 149 61 0.51476793
## 39 2020-02-29 tot_cases 70 117 47 0.50267380
## 40 2020-02-26 tot_cases 52 86 34 0.49275362
## 41 2020-03-02 tot_cases 124 192 68 0.43037975
## 42 2020-03-03 tot_cases 188 266 78 0.34361233
## 43 2020-03-04 tot_cases 255 343 88 0.29431438
## 44 2020-03-05 tot_cases 333 430 97 0.25425950
## 45 2020-03-06 tot_cases 454 562 108 0.21259843
## 46 2020-03-07 tot_cases 614 734 120 0.17804154
## 47 2020-03-08 tot_cases 823 959 136 0.15263749
## 48 2020-03-09 tot_cases 1243 1411 168 0.12660136
## 49 2020-03-10 tot_cases 1736 1931 195 0.10635397
## 50 2020-03-14 tot_cases 5140 5675 535 0.09893666
## 51 2020-03-11 tot_cases 2250 2477 227 0.09604400
## 52 2020-03-12 tot_cases 2998 3289 291 0.09257197
## 53 2020-03-13 tot_cases 3909 4284 375 0.09154156
## 54 2020-03-15 tot_cases 7342 7959 617 0.08064832
## 55 2020-03-17 tot_cases 12913 13979 1066 0.07928008
## 56 2020-03-18 tot_cases 17816 19249 1433 0.07732362
## 57 2020-03-16 tot_cases 9691 10452 761 0.07555975
## 58 2020-03-19 tot_cases 23990 25808 1818 0.07301498
## 59 2020-03-20 tot_cases 31427 33664 2237 0.06873454
## 60 2020-03-21 tot_cases 40645 43174 2529 0.06034431
## 61 2021-10-30 new_deaths 414 254 160 0.47904192
## 62 2021-10-31 new_deaths 287 185 102 0.43220339
## 63 2021-10-23 new_deaths 648 465 183 0.32884097
## 64 2021-10-24 new_deaths 329 238 91 0.32098765
## 65 2021-10-16 new_deaths 904 760 144 0.17307692
## 66 2021-02-02 new_deaths 2954 2494 460 0.16886931
## 67 2021-01-28 new_deaths 3855 3392 463 0.12777701
## 68 2021-05-31 new_deaths 222 252 30 0.12658228
## 69 2021-07-05 new_deaths 111 126 15 0.12658228
## 70 2021-01-26 new_deaths 3542 3122 420 0.12605042
## 71 2021-07-11 new_deaths 113 127 14 0.11666667
## 72 2020-11-15 new_deaths 1045 1172 127 0.11456924
## 73 2021-11-01 new_deaths 1139 1267 128 0.10640067
## 74 2021-10-09 new_deaths 1033 929 104 0.10601427
## 75 2021-07-24 new_deaths 248 275 27 0.10325048
## 76 2021-06-19 new_deaths 252 228 24 0.10000000
## 77 2020-03-20 new_deaths 95 105 10 0.10000000
## 78 2021-10-12 new_deaths 1931 1754 177 0.09606513
## 79 2021-10-02 new_deaths 1298 1183 115 0.09270455
## 80 2021-02-01 new_deaths 2283 2083 200 0.09161704
## 81 2020-12-26 new_deaths 1680 1839 159 0.09036658
## 82 2021-06-13 new_deaths 149 163 14 0.08974359
## 83 2020-12-06 new_deaths 1622 1771 149 0.08782788
## 84 2021-01-27 new_deaths 3989 3655 334 0.08738880
## 85 2021-07-08 new_deaths 264 242 22 0.08695652
## 86 2020-11-29 new_deaths 1281 1397 116 0.08663181
## 87 2021-10-17 new_deaths 676 621 55 0.08481110
## 88 2020-09-07 new_deaths 421 458 37 0.08418658
## 89 2021-08-15 new_deaths 667 724 57 0.08195543
## 90 2021-08-08 new_deaths 523 567 44 0.08073394
## 91 2021-09-25 new_deaths 1426 1316 110 0.08023341
## 92 2020-11-02 new_deaths 752 813 61 0.07795527
## 93 2020-11-28 new_deaths 1494 1614 120 0.07722008
## 94 2021-01-04 new_deaths 2086 2247 161 0.07431341
## 95 2021-06-12 new_deaths 212 197 15 0.07334963
## 96 2021-10-14 new_deaths 1539 1432 107 0.07202962
## 97 2020-10-12 new_deaths 505 542 37 0.07067813
## 98 2021-02-06 new_deaths 2756 2570 186 0.06984604
## 99 2020-10-25 new_deaths 614 658 44 0.06918239
## 100 2021-09-22 new_deaths 2274 2123 151 0.06868319
## 101 2021-09-08 new_deaths 2050 1916 134 0.06757438
## 102 2020-11-16 new_deaths 1064 1138 74 0.06721163
## 103 2020-12-27 new_deaths 1891 2022 131 0.06695630
## 104 2020-11-30 new_deaths 1610 1721 111 0.06664665
## 105 2021-09-20 new_deaths 1305 1221 84 0.06650831
## 106 2020-07-05 new_deaths 455 486 31 0.06588735
## 107 2021-10-20 new_deaths 1739 1629 110 0.06532067
## 108 2020-11-22 new_deaths 1307 1395 88 0.06513694
## 109 2020-12-25 new_deaths 2218 2367 149 0.06499455
## 110 2021-02-21 new_deaths 1545 1448 97 0.06481791
## 111 2021-01-19 new_deaths 2747 2575 172 0.06463735
## 112 2020-09-28 new_deaths 421 449 28 0.06436782
## 113 2021-09-28 new_deaths 2118 1986 132 0.06432749
## 114 2021-10-26 new_deaths 1995 1871 124 0.06414899
## 115 2020-12-07 new_deaths 2022 2156 134 0.06414552
## 116 2021-09-06 new_deaths 1063 1133 70 0.06375228
## 117 2021-09-12 new_deaths 1132 1206 74 0.06330197
## 118 2021-05-22 new_deaths 401 427 26 0.06280193
## 119 2020-12-21 new_deaths 2208 2346 138 0.06060606
## 120 2021-10-19 new_deaths 1834 1727 107 0.06009548
## 121 2020-08-24 new_deaths 608 645 37 0.05905826
## 122 2021-03-23 new_deaths 743 701 42 0.05817175
## 123 2021-08-31 new_deaths 1755 1656 99 0.05804749
## 124 2020-11-09 new_deaths 961 1018 57 0.05760485
## 125 2021-07-04 new_deaths 120 127 7 0.05668016
## 126 2021-06-29 new_deaths 278 263 15 0.05545287
## 127 2021-09-14 new_deaths 2403 2274 129 0.05516357
## 128 2021-02-12 new_deaths 2517 2382 135 0.05511329
## 129 2021-01-03 new_deaths 2034 2149 115 0.05498446
## 130 2020-03-22 new_deaths 124 131 7 0.05490196
## 131 2020-03-23 new_deaths 160 169 9 0.05471125
## 132 2020-10-09 new_deaths 792 750 42 0.05447471
## 133 2021-09-18 new_deaths 1578 1495 83 0.05401887
## 134 2021-02-05 new_deaths 3423 3245 178 0.05338932
## 135 2021-09-15 new_deaths 2388 2264 124 0.05331040
## 136 2020-12-14 new_deaths 2040 2150 110 0.05250597
## 137 2021-10-29 new_deaths 1726 1819 93 0.05246827
## 138 2021-10-15 new_deaths 1987 1887 100 0.05162623
## 139 2021-10-18 new_deaths 1262 1199 63 0.05119870
## 140 2020-08-03 new_deaths 895 942 47 0.05117039
## 141 2020-11-01 new_deaths 711 748 37 0.05071967
## 142 2021-05-27 new_deaths 548 521 27 0.05051450
## 143 2021-10-05 new_deaths 2037 1937 100 0.05032713
## 144 2020-01-22 new_cases 0 7 7 2.00000000
## 145 2020-02-27 new_cases 5 11 6 0.75000000
## 146 2020-03-01 new_cases 18 32 14 0.56000000
## 147 2020-03-02 new_cases 36 43 7 0.17721519
## 148 2021-10-30 new_cases 28712 24628 4084 0.15313086
## 149 2021-10-23 new_cases 29620 25597 4023 0.14571599
## 150 2021-05-13 new_cases 41011 35463 5548 0.14509506
## 151 2020-03-03 new_cases 64 74 10 0.14492754
## 152 2021-10-31 new_cases 20545 17870 2675 0.13926851
## 153 2020-03-04 new_cases 67 77 10 0.13888889
## 154 2021-10-24 new_cases 23600 20557 3043 0.13782639
## 155 2020-03-14 new_cases 1231 1391 160 0.12204424
## 156 2021-03-14 new_cases 47257 42079 5178 0.11592191
## 157 2021-09-12 new_cases 116283 104092 12191 0.11063868
## 158 2020-03-05 new_cases 78 87 9 0.10909091
## 159 2021-09-06 new_cases 110307 100261 10046 0.09541811
## 160 2020-03-17 new_cases 3222 3527 305 0.09038376
## 161 2020-03-13 new_cases 911 995 84 0.08814271
## 162 2020-03-06 new_cases 121 132 11 0.08695652
## 163 2020-09-06 new_cases 33988 31178 2810 0.08624129
## 164 2020-07-07 new_cases 56061 60897 4836 0.08269635
## 165 2020-10-13 new_cases 50814 55179 4365 0.08236393
## 166 2020-03-12 new_cases 748 812 64 0.08205128
## 167 2021-08-24 new_cases 163795 177621 13826 0.08099210
## 168 2020-07-26 new_cases 59006 54567 4439 0.07816999
## 169 2020-09-09 new_cases 35564 38421 2857 0.07723187
## 170 2021-09-11 new_cases 153160 141855 11305 0.07664017
## 171 2021-10-25 new_cases 94603 102106 7503 0.07628527
## 172 2020-12-29 new_cases 210249 226862 16613 0.07601273
## 173 2020-03-08 new_cases 209 225 16 0.07373272
## 174 2020-03-09 new_cases 420 452 32 0.07339450
## 175 2021-09-05 new_cases 124666 115881 8785 0.07304186
## 176 2020-03-07 new_cases 160 172 12 0.07228916
## 177 2020-03-18 new_cases 4903 5270 367 0.07215177
## 178 2020-08-09 new_cases 46359 43144 3215 0.07184117
## 179 2020-05-27 new_cases 20368 21843 1475 0.06988700
## 180 2021-09-08 new_cases 169999 181743 11744 0.06677622
## 181 2021-08-17 new_cases 150835 161030 10195 0.06538085
## 182 2021-01-05 new_cases 245060 261529 16469 0.06501918
## 183 2020-06-09 new_cases 18594 19823 1229 0.06398209
## 184 2021-09-07 new_cases 118424 111103 7321 0.06379206
## 185 2020-09-20 new_cases 34275 32172 2103 0.06329857
## 186 2020-06-02 new_cases 20731 22082 1351 0.06311167
## 187 2020-08-23 new_cases 37081 34825 2256 0.06274859
## 188 2021-09-19 new_cases 97060 91238 5822 0.06183815
## 189 2020-09-29 new_cases 38935 41374 2439 0.06074039
## 190 2020-03-19 new_cases 6174 6559 385 0.06047279
## 191 2021-08-22 new_cases 127065 119611 7454 0.06043555
## 192 2020-03-11 new_cases 514 546 32 0.06037736
## 193 2020-03-16 new_cases 2349 2493 144 0.05947955
## 194 2020-07-19 new_cases 59921 56578 3343 0.05739105
## 195 2020-09-27 new_cases 35369 33408 1961 0.05702488
## 196 2020-11-24 new_cases 175932 186115 10183 0.05625237
## 197 2021-09-22 new_cases 132374 125190 7184 0.05578419
## 198 2020-06-14 new_cases 20620 19510 1110 0.05532021
## 199 2020-10-18 new_cases 48992 46360 2632 0.05520597
## 200 2020-10-27 new_cases 75829 80102 4273 0.05480629
## 201 2020-03-20 new_cases 7437 7856 419 0.05479631
## 202 2020-08-30 new_cases 36631 34704 1927 0.05402678
## 203 2020-10-20 new_cases 58978 62239 3261 0.05380433
## 204 2020-12-01 new_cases 192552 203130 10578 0.05346718
## 205 2020-08-16 new_cases 41920 39738 2182 0.05344241
## 206 2020-03-10 new_cases 493 520 27 0.05330701
## 207 2020-07-21 new_cases 63329 66698 3369 0.05182001
## 208 2021-04-17 new_cases 78012 74105 3907 0.05136835
## 209 2020-06-16 new_cases 24926 26235 1309 0.05117179
## 210 2020-12-26 new_cases 149192 141757 7435 0.05110861
## 211 2021-08-10 new_cases 136592 143746 7154 0.05103839
## 212 2020-09-01 new_cases 38841 40860 2019 0.05066436
## 213 2021-02-07 new_cases 91485 87022 4463 0.05000364
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NM tot_deaths 1453354 1567848 114494 0.075793674
## 2 IN tot_deaths 5028752 5231738 202986 0.039566531
## 3 TN tot_deaths 4322924 4457205 134281 0.030587478
## 4 PR tot_deaths 879235 905115 25880 0.029007762
## 5 KY tot_deaths 2732412 2656872 75540 0.028033409
## 6 NC tot_deaths 4962428 4921441 40987 0.008293716
## 7 SC tot_deaths 3569982 3587043 17061 0.004767623
## 8 FL tot_deaths 15304365 15252950 51415 0.003365152
## 9 AL tot_deaths 4391384 4376851 14533 0.003314920
## 10 AL tot_cases 209779785 216389881 6610096 0.031020960
## 11 TN tot_cases 320105241 325227756 5122515 0.015875571
## 12 NC tot_cases 361373848 364334848 2961000 0.008160299
## 13 PR tot_cases 47364624 47669074 304450 0.006407201
## 14 KY tot_cases 169191874 169942936 751062 0.004429283
## 15 KY new_deaths 10662 9834 828 0.080796253
## 16 NM new_deaths 4856 5073 217 0.043710343
## 17 FL new_deaths 61112 59499 1613 0.026747146
## 18 AL new_deaths 16002 15676 326 0.020582107
## 19 NC new_deaths 18393 18130 263 0.014401884
## 20 PR new_deaths 3227 3236 9 0.002785084
## 21 KY new_cases 749093 746588 2505 0.003349645
## 22 AL new_cases 831486 833493 2007 0.002410841
## 23 PR new_cases 184589 185001 412 0.002229498
## 24 NC new_cases 1485886 1483678 2208 0.001487087
##
##
##
## Raw file for cdcDaily:
## Rows: 40,800
## Columns: 15
## $ date <date> 2021-04-01, 2021-05-31, 2020-06-13, 2021-02-02, 2021-0~
## $ state <chr> "CA", "CA", "AL", "IL", "DE", "WI", "NC", "ND", "GU", "~
## $ tot_cases <dbl> 3570660, 3685032, 25331, 1130917, 128253, 25480, 14513,~
## $ conf_cases <dbl> 3570660, 3685032, 24985, 1130917, 117969, 22932, 14513,~
## $ prob_cases <dbl> 0, 0, 346, 0, 10284, 2548, 0, 0, NA, 44, 194969, NA, NA~
## $ new_cases <dbl> 2234, 644, 959, 2304, 450, 185, 467, 133, 15, 121, 1558~
## $ pnew_case <dbl> 0, 0, 5, 0, 36, 11, 0, 0, 0, 4, 489, NA, 0, -11, 94, 0,~
## $ tot_deaths <dbl> 58090, 62011, 1062, 21336, 1920, 700, 589, 103, 5, 41, ~
## $ conf_death <dbl> 58090, 62011, 987, 19306, 1756, 694, 589, NA, NA, 38, 1~
## $ prob_death <dbl> 0, 0, 75, 2030, 164, 6, 0, NA, NA, 3, 3354, NA, 416, 76~
## $ new_deaths <dbl> 154, 5, 8, 63, 0, 2, 19, 0, 0, 9, 46, 0, 16, 66, 8, 165~
## $ pnew_death <dbl> 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 13, NA, 0, 8, 0, 0, 0, N~
## $ created_at <chr> "04/03/2021 12:00:00 AM", "06/02/2021 12:00:00 AM", "06~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "~
## $ consent_deaths <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2020-08-02 hosp_ped 4362 4629 267 0.05939273
## 2 2020-07-25 hosp_ped 4553 4325 228 0.05136292
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 ME hosp_ped 668 724 56 0.080459770
## 2 SC hosp_ped 5227 5032 195 0.038015401
## 3 WV hosp_ped 3334 3242 92 0.027980535
## 4 DE hosp_ped 2662 2595 67 0.025489823
## 5 KY hosp_ped 10521 10331 190 0.018223672
## 6 NV hosp_ped 2939 2890 49 0.016812489
## 7 KS hosp_ped 2663 2708 45 0.016756656
## 8 ID hosp_ped 2178 2146 32 0.014801110
## 9 AR hosp_ped 8128 8249 121 0.014776821
## 10 NM hosp_ped 4152 4196 44 0.010541447
## 11 NJ hosp_ped 10988 10880 108 0.009877446
## 12 NH hosp_ped 426 430 4 0.009345794
## 13 AK hosp_ped 1259 1249 10 0.007974482
## 14 MA hosp_ped 5859 5904 45 0.007651109
## 15 UT hosp_ped 4339 4306 33 0.007634471
## 16 AL hosp_ped 12888 12986 98 0.007575172
## 17 VA hosp_ped 9770 9709 61 0.006263155
## 18 MS hosp_ped 6850 6891 41 0.005967542
## 19 MO hosp_ped 22827 22713 114 0.005006588
## 20 TN hosp_ped 12536 12590 54 0.004298336
## 21 WY hosp_ped 497 495 2 0.004032258
## 22 NE hosp_ped 4381 4395 14 0.003190520
## 23 IA hosp_ped 4029 4041 12 0.002973978
## 24 AZ hosp_ped 16178 16132 46 0.002847416
## 25 IL hosp_ped 25073 25144 71 0.002827728
## 26 GA hosp_ped 32017 32084 67 0.002090451
## 27 CO hosp_ped 12851 12876 25 0.001943483
## 28 CA hosp_ped 42407 42486 79 0.001861166
## 29 PA hosp_ped 28019 28071 52 0.001854163
## 30 HI hosp_ped 1390 1392 2 0.001437815
## 31 MI hosp_ped 11782 11795 13 0.001102770
## 32 CT hosp_ped 2916 2919 3 0.001028278
## 33 NC hosp_adult 819100 818128 972 0.001187373
## 34 NM hosp_adult 151665 151488 177 0.001167727
##
##
##
## Raw file for cdcHosp:
## Rows: 34,355
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference: Administered_Dose1_Recip_5Plus Administered_Dose1_Recip_5PlusPop_Pct Series_Complete_5Plus Series_Complete_5PlusPop_Pct Administered_5Plus Admin_Per_100k_5Plus Distributed_Per_100k_5Plus Series_Complete_Moderna_5Plus Series_Complete_Pfizer_5Plus Series_Complete_Janssen_5Plus Series_Complete_Unk_Manuf_5Plus
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 29
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 22,936
## Columns: 80
## $ date <date> 2021-12-02, 2021-12-02, 2021-1~
## $ MMWR_week <dbl> 48, 48, 48, 48, 48, 48, 48, 48,~
## $ state <chr> "CO", "KS", "MT", "FM", "TN", "~
## $ Distributed <dbl> 10264195, 4840995, 1662025, 104~
## $ Distributed_Janssen <dbl> 459500, 240600, 98800, 12200, 4~
## $ Distributed_Moderna <dbl> 3874940, 1914040, 689280, 80440~
## $ Distributed_Pfizer <dbl> 5929755, 2686355, 873945, 12180~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 178237, 166168, 155507, 102443,~
## $ Distributed_Per_100k_12Plus <dbl> 207943, 197455, 181128, 132445,~
## $ Distributed_Per_100k_18Plus <dbl> 228133, 218746, 197815, 157095,~
## $ Distributed_Per_100k_65Plus <dbl> 1218430, 1018110, 805100, 21783~
## $ vxa <dbl> 8678461, 3728475, 1333956, 8004~
## $ Administered_12Plus <dbl> 8542236, 3685928, 1321921, 8003~
## $ Administered_18Plus <dbl> 8001344, 3448406, 1254563, 7790~
## $ Administered_65Plus <dbl> 2014555, 1084652, 448504, 6161,~
## $ Administered_Janssen <dbl> 307987, 121410, 54642, 12315, 2~
## $ Administered_Moderna <dbl> 3411615, 1453194, 567864, 65527~
## $ Administered_Pfizer <dbl> 4952061, 2150807, 709888, 2205,~
## $ Administered_Unk_Manuf <dbl> 6798, 3064, 1562, 2, 40505, 134~
## $ Admin_Per_100k <dbl> 150701, 127981, 124811, 78234, ~
## $ Admin_Per_100k_12Plus <dbl> 173058, 150342, 144063, 101127,~
## $ Admin_Per_100k_18Plus <dbl> 177839, 155820, 149319, 116751,~
## $ Admin_Per_100k_65Plus <dbl> 239141, 228114, 217260, 128034,~
## $ Recip_Administered <dbl> 8651457, 3830030, 1332315, 8076~
## $ Administered_Dose1_Recip <dbl> 4129805, 1936837, 641463, 47744~
## $ Administered_Dose1_Pop_Pct <dbl> 71.7, 66.5, 60.0, 46.7, 57.0, 8~
## $ Administered_Dose1_Recip_12Plus <dbl> 4016267, 1898566, 630102, 47729~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 81.4, 77.4, 68.7, 60.3, 65.9, 9~
## $ Administered_Dose1_Recip_18Plus <dbl> 3731095, 1768923, 593977, 46547~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 82.9, 79.9, 70.7, 69.8, 68.4, 9~
## $ Administered_Dose1_Recip_65Plus <dbl> 811378, 505368, 190045, 3636, 1~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 96.3, 99.9, 92.1, 75.6, 89.1, 9~
## $ vxc <dbl> 3655321, 1598588, 555986, 42465~
## $ vxcpoppct <dbl> 63.5, 54.9, 52.0, 41.5, 49.7, 5~
## $ Series_Complete_12Plus <dbl> 3633081, 1593743, 555315, 42456~
## $ Series_Complete_12PlusPop_Pct <dbl> 73.6, 65.0, 60.5, 53.6, 58.1, 6~
## $ vxcgte18 <dbl> 3379360, 1485220, 524950, 41485~
## $ vxcgte18pct <dbl> 75.1, 67.1, 62.5, 62.2, 60.5, 6~
## $ vxcgte65 <dbl> 742632, 415555, 172382, 3382, 9~
## $ vxcgte65pct <dbl> 88.2, 87.4, 83.5, 70.3, 81.7, 8~
## $ Series_Complete_Janssen <dbl> 290214, 114775, 50802, 11767, 2~
## $ Series_Complete_Moderna <dbl> 1362844, 597783, 225768, 29567,~
## $ Series_Complete_Pfizer <dbl> 2000377, 885480, 279046, 1129, ~
## $ Series_Complete_Unk_Manuf <dbl> 1886, 550, 370, 2, 9615, 582, 7~
## $ Series_Complete_Janssen_12Plus <dbl> 290187, 114760, 50796, 11759, 2~
## $ Series_Complete_Moderna_12Plus <dbl> 1362821, 597774, 225749, 29566,~
## $ Series_Complete_Pfizer_12Plus <dbl> 1978190, 880660, 278400, 1129, ~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 1883, 549, 370, 2, 9609, 581, 7~
## $ Series_Complete_Janssen_18Plus <dbl> 288996, 114657, 50744, 11744, 2~
## $ Series_Complete_Moderna_18Plus <dbl> 1359133, 597429, 225649, 29562,~
## $ Series_Complete_Pfizer_18Plus <dbl> 1729411, 772654, 248194, 177, 1~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 1820, 480, 363, 2, 9529, 540, 7~
## $ Series_Complete_Janssen_65Plus <dbl> 25892, 17852, 9692, 1064, 34549~
## $ Series_Complete_Moderna_65Plus <dbl> 354554, 198118, 82734, 2310, 46~
## $ Series_Complete_Pfizer_65Plus <dbl> 361502, 199350, 79772, 8, 43133~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 684, 235, 184, 0, 5161, 264, 35~
## $ Additional_Doses <dbl> 1040424, 372165, 166815, 1243, ~
## $ Additional_Doses_Vax_Pct <dbl> 28.5, 23.3, 30.0, 2.9, 24.5, 15~
## $ Additional_Doses_18Plus <dbl> 1039133, 371717, 166591, 1240, ~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 30.7, 25.0, 31.7, 3.0, 25.8, 16~
## $ Additional_Doses_50Plus <dbl> 709407, 285860, 130719, 527, 65~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 45.2, 35.7, 42.5, 4.2, 36.1, 22~
## $ Additional_Doses_65Plus <dbl> 441998, 195358, 91429, 161, 448~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 59.5, 47.0, 53.0, 4.8, 48.0, 29~
## $ Additional_Doses_Moderna <dbl> 471885, 162870, 75401, 1197, 34~
## $ Additional_Doses_Pfizer <dbl> 554745, 204374, 88538, 10, 4721~
## $ Additional_Doses_Janssen <dbl> 13664, 4833, 2660, 36, 10712, 4~
## $ Additional_Doses_Unk_Manuf <dbl> 130, 88, 216, 0, 1676, 28, 628,~
## $ Administered_Dose1_Recip_5Plus <dbl> 4129531, 1936736, 641376, 47739~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 76.1, 71.0, 63.7, 51.4, 60.6, 8~
## $ Series_Complete_5Plus <dbl> 3655266, 1598570, 555960, 42461~
## $ Series_Complete_5PlusPop_Pct <dbl> 67.4, 58.6, 55.2, 45.7, 52.8, 6~
## $ Administered_5Plus <dbl> 8678154, 3728366, 1333834, 8004~
## $ Admin_Per_100k_5Plus <dbl> 159921, 136671, 132374, 86235, ~
## $ Distributed_Per_100k_5Plus <dbl> 189148, 177457, 164945, 112927,~
## $ Series_Complete_Moderna_5Plus <dbl> 1362832, 597775, 225752, 29567,~
## $ Series_Complete_Pfizer_5Plus <dbl> 2000356, 885475, 279041, 1129, ~
## $ Series_Complete_Janssen_5Plus <dbl> 290192, 114770, 50797, 11763, 2~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 1886, 550, 370, 2, 9613, 582, 7~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.34e+10 2.50e+8 4.83e+7 767724 40120
## 2 after 1.34e+10 2.48e+8 4.81e+7 764111 34680
## 3 pctchg 4.37e- 3 4.23e-3 4.45e-3 0.00471 0.136
##
##
## Processed for cdcDaily:
## Rows: 34,680
## Columns: 6
## $ date <date> 2021-04-01, 2021-05-31, 2020-06-13, 2021-02-02, 2021-09-19~
## $ state <chr> "CA", "CA", "AL", "IL", "DE", "WI", "NC", "ND", "AL", "AL",~
## $ tot_cases <dbl> 3570660, 3685032, 25331, 1130917, 128253, 25480, 14513, 660~
## $ tot_deaths <dbl> 58090, 62011, 1062, 21336, 1920, 700, 589, 103, 41, 14952, ~
## $ new_cases <dbl> 2234, 644, 959, 2304, 450, 185, 467, 133, 121, 1558, 899, 6~
## $ new_deaths <dbl> 154, 5, 8, 63, 0, 2, 19, 0, 9, 46, 16, 66, 8, 165, 33, 0, 1~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.67e+7 3.06e+7 686791 34355
## 2 after 3.66e+7 3.05e+7 672548 33003
## 3 pctchg 5.03e-3 4.87e-3 0.0207 0.0394
##
##
## Processed for cdcHosp:
## Rows: 33,003
## Columns: 5
## $ date <date> 2020-10-14, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "ID", "NE", "DC", "NH", "HI", "ID", "KS", "NH", "KY", "NV",~
## $ inp <dbl> 221, 376, 154, 45, 110, 194, 474, 52, 623, 501, 1213, 302, ~
## $ hosp_adult <dbl> 219, 367, 136, 44, 108, 194, 454, 52, 611, 500, 1180, 291, ~
## $ hosp_ped <dbl> 2, 9, 18, 1, 2, 0, 5, 0, 12, 1, 32, 6, 9, 6, 1, 33, 8, 9, 2~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.83e+11 7.97e+10 709132. 2.25e+10 1150103. 7.60e+10 858768.
## 2 after 8.74e+10 3.85e+10 596524. 1.09e+10 1041832. 3.67e+10 731409.
## 3 pctchg 5.22e- 1 5.16e- 1 0.159 5.16e- 1 0.0941 5.17e- 1 0.148
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 18,054
## Columns: 9
## $ date <date> 2021-12-02, 2021-12-02, 2021-12-02, 2021-12-02, 2021-12-0~
## $ state <chr> "CO", "KS", "MT", "TN", "PA", "IL", "HI", "ID", "MS", "FL"~
## $ vxa <dbl> 8678461, 3728475, 1333956, 8234345, 18442499, 17941198, 20~
## $ vxc <dbl> 3655321, 1598588, 555986, 3392571, 7513481, 7805286, 86708~
## $ vxcpoppct <dbl> 63.5, 54.9, 52.0, 49.7, 58.7, 61.6, 61.2, 45.3, 47.1, 61.6~
## $ vxcgte65 <dbl> 742632, 415555, 172382, 934141, 2036584, 1759738, 238119, ~
## $ vxcgte65pct <dbl> 88.2, 87.4, 83.5, 81.7, 85.1, 86.1, 88.7, 84.0, 81.1, 87.9~
## $ vxcgte18 <dbl> 3379360, 1485220, 524950, 3216071, 7061682, 7205279, 81111~
## $ vxcgte18pct <dbl> 75.1, 67.1, 62.5, 60.5, 69.5, 73.1, 72.7, 60.4, 58.0, 72.4~
##
## Integrated per capita data file:
## Rows: 34,893
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211202)The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_211224.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_211224.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_211224.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_211202")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_211202")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_211202")$dfRaw$vax
)
cdc_daily_211224 <- readRunCDCDaily(thruLabel="Dec 23, 2021",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 21
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-11-28 new_deaths 180 131 49 0.31511254
## 2 2021-11-27 new_deaths 207 153 54 0.30000000
## 3 2021-11-21 new_deaths 174 143 31 0.19558360
## 4 2021-11-25 new_deaths 453 416 37 0.08515535
## 5 2021-11-20 new_deaths 358 331 27 0.07837446
## 6 2021-11-14 new_deaths 214 198 16 0.07766990
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KY tot_deaths 3131235 3044811 86424 0.027986838
## 2 MP tot_deaths 1266 1263 3 0.002372479
## 3 AL tot_deaths 4865040 4858536 6504 0.001337779
## 4 MP tot_cases 87102 84054 3048 0.035616630
## 5 KY new_deaths 11768 10987 781 0.068644254
## 6 AL new_deaths 16309 16157 152 0.009363642
## 7 NC new_deaths 18915 18776 139 0.007375766
## 8 FL new_deaths 62080 61630 450 0.007275079
## 9 MP new_cases 855 395 460 0.736000000
## 10 KY new_cases 792201 789102 3099 0.003919552
## 11 NC new_cases 1539610 1537044 2566 0.001668046
##
##
##
## Raw file for cdcDaily:
## Rows: 42,060
## Columns: 15
## $ date <date> 2021-12-01, 2021-04-01, 2021-05-31, 2020-12-08, 2021-0~
## $ state <chr> "ND", "CA", "CA", "AL", "IL", "DE", "WI", "NC", "ND", "~
## $ tot_cases <dbl> 163565, 3570660, 3685032, 277175, 1130917, 128253, 2548~
## $ conf_cases <dbl> 135705, 3570660, 3685032, 231203, 1130917, 117969, 2293~
## $ prob_cases <dbl> 27860, 0, 0, 45972, 0, 10284, 2548, 4922, 0, NA, NA, NA~
## $ new_cases <dbl> 589, 2234, 644, 3513, 2304, 450, 185, 1801, 133, 15, 17~
## $ pnew_case <dbl> 220, 0, 0, 921, 0, 36, 11, 114, 0, 0, 0, 0, NA, 0, 94, ~
## $ tot_deaths <dbl> 1907, 58090, 62011, 5729, 21336, 1920, 700, 3656, 103, ~
## $ conf_death <dbl> NA, 58090, 62011, 4818, 19306, 1756, 694, 3618, NA, NA,~
## $ prob_death <dbl> NA, 0, 0, 911, 2030, 164, 6, 38, NA, NA, NA, 157, NA, 1~
## $ new_deaths <dbl> 9, 154, 5, 55, 63, 0, 2, 25, 0, 0, -1, 7, 0, 0, 8, 165,~
## $ pnew_death <dbl> 0, 0, 0, 10, 16, 0, 0, 1, 0, 0, 0, 0, NA, 0, 0, 0, 0, -~
## $ created_at <chr> "12/02/2021 02:35:20 PM", "04/03/2021 12:00:00 AM", "06~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Agree", "~
## $ consent_deaths <chr> "Not agree", "Agree", "Agree", "Agree", "Agree", "Agree~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 21
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 KS inp 274294 273824 470 0.001714959
## 2 ME hosp_ped 781 733 48 0.063408190
## 3 KS hosp_ped 2947 2798 149 0.051871192
## 4 NV hosp_ped 2998 3083 85 0.027955928
## 5 DE hosp_ped 2747 2813 66 0.023741007
## 6 SC hosp_ped 5316 5441 125 0.023240680
## 7 VA hosp_ped 10072 10229 157 0.015467218
## 8 ID hosp_ped 2261 2295 34 0.014925373
## 9 TN hosp_ped 13050 12875 175 0.013500482
## 10 MA hosp_ped 6271 6187 84 0.013485311
## 11 NJ hosp_ped 11368 11519 151 0.013195264
## 12 AR hosp_ped 8372 8448 76 0.009036861
## 13 AZ hosp_ped 17671 17524 147 0.008353459
## 14 WV hosp_ped 3485 3509 24 0.006863025
## 15 CO hosp_ped 14015 13924 91 0.006514192
## 16 IN hosp_ped 11243 11311 68 0.006029973
## 17 NM hosp_ped 4590 4564 26 0.005680577
## 18 WY hosp_ped 535 538 3 0.005591799
## 19 VT hosp_ped 184 185 1 0.005420054
## 20 AL hosp_ped 13252 13323 71 0.005343368
## 21 SD hosp_ped 3048 3033 15 0.004933399
## 22 UT hosp_ped 4886 4908 22 0.004492546
## 23 KY hosp_ped 11608 11557 51 0.004403194
## 24 MS hosp_ped 7227 7199 28 0.003881880
## 25 IA hosp_ped 4386 4376 10 0.002282584
## 26 PA hosp_ped 31152 31221 69 0.002212496
## 27 MO hosp_ped 24222 24273 51 0.002103310
## 28 NH hosp_ped 481 482 1 0.002076843
## 29 HI hosp_ped 1445 1448 3 0.002073972
## 30 FL hosp_ped 70884 70741 143 0.002019417
## 31 PR hosp_ped 14228 14200 28 0.001969889
## 32 TX hosp_ped 69942 69819 123 0.001760148
## 33 WI hosp_ped 6240 6233 7 0.001122424
## 34 KS hosp_adult 247830 247511 319 0.001288002
##
##
##
## Raw file for cdcHosp:
## Rows: 35,489
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 21
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 24,280
## Columns: 80
## $ date <date> 2021-12-23, 2021-12-23, 2021-1~
## $ MMWR_week <dbl> 51, 51, 51, 51, 51, 51, 51, 51,~
## $ state <chr> "AK", "SC", "US", "NJ", "UT", "~
## $ Distributed <dbl> 1322225, 8828335, 611897975, 18~
## $ Distributed_Janssen <dbl> 83100, 435600, 29391500, 894100~
## $ Distributed_Moderna <dbl> 506560, 3506920, 222509200, 658~
## $ Distributed_Pfizer <dbl> 732565, 4885815, 359997275, 110~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 180744, 171467, 184302, 208115,~
## $ Distributed_Per_100k_12Plus <dbl> 217158, 199737, 215822, 242639,~
## $ Distributed_Per_100k_18Plus <dbl> 239724, 218657, 236931, 266219,~
## $ Distributed_Per_100k_65Plus <dbl> 1443670, 942168, 1116770, 12527~
## $ vxa <dbl> 1015137, 6623650, 500222330, 14~
## $ Administered_12Plus <dbl> 989664, 6538665, 489911859, 143~
## $ Administered_18Plus <dbl> 925534, 6212540, 460223551, 134~
## $ Administered_65Plus <dbl> 205343, 2130888, 130509675, 347~
## $ Administered_Janssen <dbl> 40794, 215367, 17501026, 525052~
## $ Administered_Moderna <dbl> 395015, 2600706, 190891698, 563~
## $ Administered_Pfizer <dbl> 578479, 3805535, 291318945, 852~
## $ Administered_Unk_Manuf <dbl> 849, 2042, 510661, 907, 295, 13~
## $ Admin_Per_100k <dbl> 138766, 128647, 150665, 165339,~
## $ Admin_Per_100k_12Plus <dbl> 162539, 147934, 172796, 188596,~
## $ Admin_Per_100k_18Plus <dbl> 167802, 153870, 178202, 193386,~
## $ Admin_Per_100k_65Plus <dbl> 224203, 227410, 238191, 235477,~
## $ Recip_Administered <dbl> 1013631, 6599301, 500222330, 15~
## $ Administered_Dose1_Recip <dbl> 473411, 3208049, 241520561, 735~
## $ Administered_Dose1_Pop_Pct <dbl> 64.7, 62.3, 72.7, 82.8, 66.9, 7~
## $ Administered_Dose1_Recip_12Plus <dbl> 456849, 3152140, 235109931, 715~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 75.0, 71.3, 82.9, 93.9, 79.5, 8~
## $ Administered_Dose1_Recip_18Plus <dbl> 423349, 2975205, 219232193, 664~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 76.8, 73.7, 84.9, 95.0, 82.0, 8~
## $ Administered_Dose1_Recip_65Plus <dbl> 85187, 924673, 55658469, 155410~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 93.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 410287, 2721525, 204740321, 622~
## $ vxcpoppct <dbl> 56.1, 52.9, 61.7, 70.1, 58.4, 5~
## $ Series_Complete_12Plus <dbl> 401419, 2689999, 200881586, 610~
## $ Series_Complete_12PlusPop_Pct <dbl> 65.9, 60.9, 70.9, 80.2, 70.4, 6~
## $ vxcgte18 <dbl> 372484, 2540559, 187461696, 566~
## $ vxcgte18pct <dbl> 67.5, 62.9, 72.6, 81.6, 72.7, 6~
## $ vxcgte65 <dbl> 77057, 796345, 47915719, 132439~
## $ vxcgte65pct <dbl> 84.1, 85.0, 87.5, 89.8, 90.1, 9~
## $ Series_Complete_Janssen <dbl> 36800, 195966, 16265233, 489870~
## $ Series_Complete_Moderna <dbl> 150339, 995798, 73147737, 22128~
## $ Series_Complete_Pfizer <dbl> 223048, 1529419, 115189845, 352~
## $ Series_Complete_Unk_Manuf <dbl> 100, 342, 137506, 392, 9, 30, 5~
## $ Series_Complete_Janssen_12Plus <dbl> 36796, 195922, 16260793, 489790~
## $ Series_Complete_Moderna_12Plus <dbl> 150334, 995686, 73142638, 22127~
## $ Series_Complete_Pfizer_12Plus <dbl> 214189, 1498049, 111341322, 340~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 100, 342, 136833, 391, 9, 30, 5~
## $ Series_Complete_Janssen_18Plus <dbl> 36657, 195368, 16235127, 489546~
## $ Series_Complete_Moderna_18Plus <dbl> 149942, 993747, 73062889, 22121~
## $ Series_Complete_Pfizer_18Plus <dbl> 185791, 1351104, 98030208, 2966~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 94, 340, 133472, 372, 9, 30, 56~
## $ Series_Complete_Janssen_65Plus <dbl> 3475, 30466, 2308213, 90977, 16~
## $ Series_Complete_Moderna_65Plus <dbl> 42431, 340254, 22853608, 616897~
## $ Series_Complete_Pfizer_65Plus <dbl> 31112, 425424, 22695193, 616389~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 39, 201, 58705, 136, 1, 20, 310~
## $ Additional_Doses <dbl> 136704, 798606, 64475171, 19601~
## $ Additional_Doses_Vax_Pct <dbl> 33.3, 29.3, 31.5, 31.5, 27.9, 3~
## $ Additional_Doses_18Plus <dbl> 135787, 796080, 64192194, 19496~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 36.5, 31.3, 34.2, 34.4, 31.4, 3~
## $ Additional_Doses_50Plus <dbl> 89008, 633195, 45118229, 133206~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 51.1, 42.3, 46.2, 45.9, 47.8, 4~
## $ Additional_Doses_65Plus <dbl> 48410, 422282, 27104857, 741134~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 62.8, 53.0, 56.6, 56.0, 60.0, 5~
## $ Additional_Doses_Moderna <dbl> 62743, 338668, 28709418, 883192~
## $ Additional_Doses_Pfizer <dbl> 72204, 443362, 34759759, 103482~
## $ Additional_Doses_Janssen <dbl> 1739, 16126, 991941, 42146, 751~
## $ Additional_Doses_Unk_Manuf <dbl> 18, 450, 14053, 18, 1, 8, 10, 5~
## $ Administered_Dose1_Recip_5Plus <dbl> 473349, 3206908, 241480397, 735~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 69.6, 66.0, 77.3, 87.9, 72.5, 7~
## $ Series_Complete_5Plus <dbl> 410274, 2720990, 204730406, 622~
## $ Series_Complete_5PlusPop_Pct <dbl> 60.3, 56.0, 65.6, 74.4, 63.2, 6~
## $ Administered_5Plus <dbl> 1015062, 6621955, 500174369, 14~
## $ Admin_Per_100k_5Plus <dbl> 149172, 136359, 160176, 175499,~
## $ Distributed_Per_100k_5Plus <dbl> 194312, 181793, 195955, 220916,~
## $ Series_Complete_Moderna_5Plus <dbl> 150336, 995723, 73144600, 22127~
## $ Series_Complete_Pfizer_5Plus <dbl> 223042, 1529001, 115186044, 352~
## $ Series_Complete_Janssen_5Plus <dbl> 36796, 195924, 16262294, 489818~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 100, 342, 137468, 392, 9, 30, 5~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.45e+10 2.66e+8 5.12e+7 793663 41359
## 2 after 1.44e+10 2.65e+8 5.10e+7 790023 35751
## 3 pctchg 4.37e- 3 4.25e-3 4.63e-3 0.00459 0.136
##
##
## Processed for cdcDaily:
## Rows: 35,751
## Columns: 6
## $ date <date> 2021-12-01, 2021-04-01, 2021-05-31, 2020-12-08, 2021-02-02~
## $ state <chr> "ND", "CA", "CA", "AL", "IL", "DE", "WI", "NC", "ND", "NE",~
## $ tot_cases <dbl> 163565, 3570660, 3685032, 277175, 1130917, 128253, 25480, 2~
## $ tot_deaths <dbl> 1907, 58090, 62011, 5729, 21336, 1920, 700, 3656, 103, 282,~
## $ new_cases <dbl> 589, 2234, 644, 3513, 2304, 450, 185, 1801, 133, 179, 798, ~
## $ new_deaths <dbl> 9, 154, 5, 55, 63, 0, 2, 25, 0, -1, 7, 0, 0, 8, 165, 0, 41,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.81e+7 3.20e+7 721869 35489
## 2 after 3.80e+7 3.19e+7 707444 34074
## 3 pctchg 4.88e-3 4.69e-3 0.0200 0.0399
##
##
## Processed for cdcHosp:
## Rows: 34,074
## Columns: 5
## $ date <date> 2020-10-16, 2020-10-14, 2020-10-11, 2020-10-10, 2020-10-09~
## $ state <chr> "NH", "HI", "HI", "NM", "HI", "DC", "KS", "NH", "LA", "MO",~
## $ inp <dbl> 38, 111, 99, 184, 110, 166, 474, 52, 549, 1208, 1185, 156, ~
## $ hosp_adult <dbl> 38, 111, 99, 173, 108, 149, 454, 52, 537, 1174, 1143, 143, ~
## $ hosp_ped <dbl> 0, 0, 0, 11, 2, 17, 5, 0, 12, 32, 41, 13, 0, 17, 1, 0, 2, 2~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.03e+11 8.83e+10 785079. 2.46e+10 1258189. 8.39e+10 948625.
## 2 after 9.74e+10 4.27e+10 660214. 1.19e+10 1135392. 4.06e+10 806974.
## 3 pctchg 5.21e- 1 5.17e- 1 0.159 5.16e- 1 0.0976 5.17e- 1 0.149
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 19,125
## Columns: 9
## $ date <date> 2021-12-23, 2021-12-23, 2021-12-23, 2021-12-23, 2021-12-2~
## $ state <chr> "AK", "SC", "NJ", "UT", "SD", "DE", "MI", "MT", "WI", "PA"~
## $ vxa <dbl> 1015137, 6623650, 14685687, 4444635, 1243823, 1564474, 137~
## $ vxc <dbl> 410287, 2721525, 6226479, 1870704, 501708, 621891, 5644292~
## $ vxcpoppct <dbl> 56.1, 52.9, 70.1, 58.4, 56.7, 63.9, 56.5, 53.8, 61.7, 63.5~
## $ vxcgte65 <dbl> 77057, 796345, 1324399, 329535, 138881, 175050, 1522802, 1~
## $ vxcgte65pct <dbl> 84.1, 85.0, 89.8, 90.1, 91.4, 92.7, 86.3, 84.1, 93.3, 90.8~
## $ vxcgte18 <dbl> 372484, 2540559, 5668559, 1654379, 461584, 573855, 5209125~
## $ vxcgte18pct <dbl> 67.5, 62.9, 81.6, 72.7, 69.1, 74.5, 66.4, 63.5, 72.3, 73.8~
##
## Integrated per capita data file:
## Rows: 35,964
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_211224)Code is also written to download the latest data by individual hospital:
indivHospFile <- "./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv"
# Download the file
fileDownload(indivHospFile, url="https://healthdata.gov/api/views/anag-cw7u/rows.csv?accessType=DOWNLOAD")## size isdir mode
## ./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv 152346833 FALSE 666
## mtime
## ./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv 2021-12-25 09:53:06
## ctime
## ./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv 2021-12-25 09:52:06
## atime exe
## ./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv 2021-12-25 09:53:06 no# Read the file and glimpse
indivHosp_20211225 <- fileRead(indivHospFile)##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.glimpse(indivHosp_20211225)## Rows: 359,743
## Columns: 106
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~# Save in RDS format for future processing
saveToRDS(indivHosp_20211225)Checks are run on some of the key fields in the data, including:
- hospital_subtype - type of hospital
- total_beds_7_day_avg - average number of staffed inpatient and outpatient beds, including overflow
- all_adult_hospital_beds_7_day_avg - average number of staffed inpatient and outpatient beds, including overflow
- all_adult_hospital_inpatient_bed_occupied_7_day_avg - average number occupied
- total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg - average number occupied COVID positive
- inpatient_beds_7_day_avg - average number of staffed inpatient beds
- total_icu_beds_7_day_avg - average number of staffed ICU beds
- icu_beds_used_7_day_avg - average number of ICU beds occupied
- staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg - adult COVID ICU beds occupied
# Hospital demographic data
indivHosp_20211225 %>% count(hospital_subtype)## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 6757
## 2 Critical Access Hospitals 96111
## 3 Long Term 24724
## 4 Short Term 232151indivHosp_20211225 %>% count(state) %>% filter(!(state %in% c(state.abb, "DC")))## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 17
## 2 GU 144
## 3 MP 72
## 4 PR 3976
## 5 VI 144# Hospital beds average
hhsMapper <- c("total_beds_7_day_avg"="total_beds",
"all_adult_hospital_beds_7_day_avg"="adult_beds",
"all_adult_hospital_inpatient_bed_occupied_7_day_avg"="adult_beds_occupied",
"total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg"="adult_beds_covid",
"inpatient_beds_7_day_avg"="inpatient_beds",
"total_icu_beds_7_day_avg"="icu_beds",
"icu_beds_used_7_day_avg"="icu_beds_occupied",
"staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg"="adult_icu_covid"
)
# Histograms by metric
indivHosp_20211225 %>%
select(names(hhsMapper)) %>%
pivot_longer(-c()) %>%
ggplot(aes(x=value)) +
geom_histogram(fill="lightblue") +
facet_wrap(~name, scales="free")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 22873 rows containing non-finite values (stat_bin).
# Histograms by metric, excluding NA and -999999
indivHosp_20211225 %>%
select(names(hhsMapper)) %>%
pivot_longer(-c()) %>%
filter(!is.na(value), value != -999999) %>%
ggplot(aes(x=value)) +
geom_histogram(fill="lightblue") +
facet_wrap(~hhsMapper[name], scales="free")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Key metrics for a select hospital
set.seed(2112261542)
keyHosp <- indivHosp_20211225 %>% pull(hospital_pk) %>% sample(1)
keyHospName <- indivHosp_20211225 %>%
filter(hospital_pk %in% all_of(keyHosp), collection_week==max(collection_week)) %>%
mutate(useName=paste0(hospital_name, " (code: ", keyHosp, ") ",city, ", ", state, " ", zip)) %>%
pull(useName)
indivHosp_20211225 %>%
filter(hospital_pk %in% all_of(keyHosp)) %>%
select(date=collection_week, names(hhsMapper)) %>%
pivot_longer(-c(date)) %>%
filter(!is.na(value), value != -999999) %>%
ggplot(aes(x=date, y=value)) +
geom_line() +
facet_wrap(~hhsMapper[name], scales="free_y") +
lims(y=c(0, NA)) +
labs(x=NULL, y="Average weekly value", title=keyHospName)
The key hospital is plotted for similar metrics:
# Adult Beds
indivHosp_20211225 %>%
filter(hospital_pk %in% all_of(keyHosp)) %>%
select(date=collection_week, names(hhsMapper)[1:4]) %>%
colRenamer(vecRename=hhsMapper[1:4]) %>%
pivot_longer(-c(date)) %>%
filter(!is.na(value), value != -999999) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., !(name %in% c("total_beds", "adult_beds"))), aes(fill=name), position="identity") +
geom_line(data=~filter(., name %in% c("total_beds", "adult_beds")),
aes(group=name, color=name),
size=1.5
) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Average weekly value", title=keyHospName, subtitle="Adult beds") +
scale_color_manual("Capacity", values=c("adult_beds"="black", "total_beds"="green")) +
scale_fill_manual("Occupied", values=c("adult_beds_covid"="red", "adult_beds_occupied"="lightblue"))
# Adult ICU Beds
indivHosp_20211225 %>%
filter(hospital_pk %in% all_of(keyHosp)) %>%
select(date=collection_week, names(hhsMapper)[6:8]) %>%
colRenamer(vecRename=hhsMapper[6:8]) %>%
pivot_longer(-c(date)) %>%
filter(!is.na(value), value != -999999) %>%
ggplot(aes(x=date, y=value)) +
geom_col(data=~filter(., !(name %in% c("icu_beds"))), aes(fill=name), position="identity") +
geom_line(data=~filter(., name %in% c("icu_beds")), aes(group=name, color=name), size=1.5) +
lims(y=c(0, NA)) +
labs(x=NULL, y="Average weekly value", title=keyHospName, subtitle="ICU beds") +
scale_color_manual("Capacity", values=c("icu_beds"="black")) +
scale_fill_manual("Occupied", values=c("adult_icu_covid"="red", "icu_beds_occupied"="lightblue"))
The process is converted to functional form:
plotHospitalUtilization <- function(df,
keyHosp=NULL,
plotTitle=NULL,
seed=2112261542,
varMap=hhsMapper,
createFacets=TRUE,
p2List=list("Adult Beds"=list("colsPlot"=c("adult_beds_occupied"="lightblue",
"adult_beds_covid"="red"
),
"linesPlot"=c("adult_beds"="black",
"total_beds"="green"
)
),
"ICU Beds"=list("colsPlot"=c("icu_beds_occupied"="lightblue",
"adult_icu_covid"="red"
),
"linesPlot"=c("icu_beds"="black")
)
),
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df: file containing hospital utilization data
# keyHosp: character vector of hospital_pk to use (NULL means select one at random using seed)
# plotTitle: title to use for plots (NULL means use a default based on keyHosp)
# seed: random seed to use for selecting a hospital
# varMap: character mapping file of format c("variable name"="plotting facet name")
# createFacets: boolean, should the facetted plots be create?
# returnData: boolean, should plot data be returned?
# Sample a keyHosp if not provided
if(is.null(keyHosp)) {
set.seed(seed)
keyHosp <- df %>%
pull(hospital_pk) %>%
sample(1)
}
# Get plotTitle if not provided
if(is.null(plotTitle)) {
if(length(keyHosp) > 1) plotTitle <- "Multiple hospitals combined"
else {
plotTitle <- df %>%
filter(hospital_pk %in% all_of(keyHosp), collection_week==max(collection_week)) %>%
mutate(useName=paste0(hospital_name, " (code: ", keyHosp, ") ",city, ", ", state, " ", zip)) %>%
pull(useName)
}
}
# Create key plot data
p1Data <- df %>%
filter(hospital_pk %in% all_of(keyHosp)) %>%
select(date=collection_week, names(varMap)) %>%
colRenamer(vecRename=varMap) %>%
pivot_longer(-c(date)) %>%
filter(!is.na(value), value != -999999) %>%
group_by(date, name) %>%
summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
# Create the facetted plots if requested
if(isTRUE(createFacets)) {
# Create the key plot
p1 <- p1Data %>%
ggplot(aes(x=date, y=value)) +
geom_line() +
facet_wrap(~name, scales="free_y") +
lims(y=c(0, NA)) +
labs(x=NULL, y="Average weekly value", title=plotTitle)
print(p1)
}
# Create the stacked bar plots
for(plotType in names(p2List)) {
# Create the base plot
p2 <- ggplot(data=p1Data, aes(x=date, y=value))
# Add the columns
for(vCol in names(p2List[[plotType]][["colsPlot"]])) {
p2 <- p2 + geom_col(data=mutate(filter(p1Data, name %in% vCol), fill=vCol), aes(fill=fill))
}
# Add the lines
p2 <- p2 + geom_line(data=filter(p1Data, name %in% names(p2List[[plotType]][["linesPlot"]])),
aes(group=name, color=name),
size=1.5
)
# Add the limits, labels, and scales
p2 <- p2 +
lims(y=c(0, NA)) +
labs(x=NULL, y="Average weekly value", title=plotTitle, subtitle=plotType) +
scale_color_manual("Capacity", values=p2List[[plotType]][["linesPlot"]]) +
scale_fill_manual("Occupied", values=p2List[[plotType]][["colsPlot"]])
# Print the plot
print(p2)
}
# Return the data if requested
if(isTRUE(returnData)) return(p1Data)
}
# Random hospital selection, full plots
plotHospitalUtilization(indivHosp_20211225)
plotHospitalUtilization(indivHosp_20211225, seed=2112281733)
# Random hospital selection, one plot or data
plotHospitalUtilization(indivHosp_20211225, createFacets=FALSE)
plotHospitalUtilization(indivHosp_20211225, p2List=list())
plotHospitalUtilization(indivHosp_20211225, createFacets=FALSE, p2List=list(), returnData=TRUE)## # A tibble: 494 x 4
## date name value n
## <date> <chr> <dbl> <int>
## 1 2020-07-31 adult_beds 76 1
## 2 2020-07-31 adult_beds_occupied 57.4 1
## 3 2020-07-31 icu_beds 16 1
## 4 2020-07-31 icu_beds_occupied 12.6 1
## 5 2020-07-31 inpatient_beds 76 1
## 6 2020-07-31 total_beds 76 1
## 7 2020-08-07 adult_beds 74.6 1
## 8 2020-08-07 adult_beds_occupied 51.9 1
## 9 2020-08-07 icu_beds 16 1
## 10 2020-08-07 icu_beds_occupied 12.7 1
## # ... with 484 more rows# Combination of hospitals, full plots
indivHosp_20211225 %>%
filter(state=="FL", collection_week==max(collection_week)) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20211225, keyHosp=., plotTitle="Florida Hospitals Summed")
The process to download and read data is converted to functional form:
# Hospital beds average
hhsMapper <- c("total_beds_7_day_avg"="total_beds",
"all_adult_hospital_beds_7_day_avg"="adult_beds",
"all_adult_hospital_inpatient_bed_occupied_7_day_avg"="adult_beds_occupied",
"total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg"="adult_beds_covid",
"inpatient_beds_7_day_avg"="inpatient_beds",
"total_icu_beds_7_day_avg"="icu_beds",
"icu_beds_used_7_day_avg"="icu_beds_occupied",
"staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg"="adult_icu_covid"
)
downloadReadHospitalData <- function(loc,
url="https://healthdata.gov/api/views/anag-cw7u/rows.csv?accessType=DOWNLOAD",
ovrWrite=FALSE,
mapper=hhsMapper
) {
# FUNCTION ARGUMENTS:
# loc: location for the downloaded data
# url location for downloading data
# ovrWrite boolean, if loc exists, should it be overwritten?
# mapper: character vector of form c("variable"="formatted name") of variables to run histograms for
# Check if the file exists, download if appropriate
tempDownload <- function(x=loc, y=url, z=ovrWrite) {
if(file.exists(x)) {
cat("\nFile", x, "already exists\n")
if(!isTRUE(z)) {
cat("File will not be downloaded since ovrWrite is not TRUE\n")
return()
}
}
# Download the file
fileDownload(x, url=y, ovrWrite=z)
}
tempDownload()
# Read the file and glimpse
df <- fileRead(loc)
glimpse(df)
# Basic count checks
cat("\nHospital Subtype Counts:\n")
df %>% count(hospital_subtype) %>% print()
cat("\nRecords other than 50 states and DC\n")
df %>% count(state) %>% filter(!(state %in% c(state.abb, "DC"))) %>% print()
# Counts of less than 0, NA, and -999999
cat("\nRecord types for key metrics\n")
df %>%
select(names(mapper)) %>%
pivot_longer(-c()) %>%
mutate(type=case_when(is.na(value) ~ "NA",
value==-999999 ~ "Value -999999",
value < 0 ~ "Negative",
TRUE ~ "Positive"
)
) %>%
count(name, type) %>%
pivot_wider(name, names_from="type", values_from="n", values_fill=0) %>%
group_by(name) %>%
mutate(Total=sum(across(where(is.numeric)))) %>%
ungroup() %>%
print()
# Basic Histograms (NA and -999999 are missing data)
p1 <- df %>%
select(names(mapper)) %>%
pivot_longer(-c()) %>%
filter(!is.na(value), value != -999999, value >= 0) %>%
ggplot(aes(x=value/1000)) +
geom_histogram(fill="lightblue") +
scale_x_sqrt() +
labs(x="Value (000s)",
y="# non-missing records",
title="Histogram for key metrics by record",
subtitle="Excludes values less than 0, as well as NA or -999999"
) +
facet_wrap(~hhsMapper[name], scales="free")
print(p1)
# Return the file
df
}The process is run and cached for reduced processing time:
# Example using existing data
downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20211225.csv already exists
## File will not be downloaded since ovrWrite is not TRUE##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 359,743
## Columns: 106
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 6757
## 2 Critical Access Hospitals 96111
## 3 Long Term 24724
## 4 Short Term 232151
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 17
## 2 GU 144
## 3 MP 72
## 4 PR 3976
## 5 VI 144
##
## Record types for key metrics
## # A tibble: 8 x 6
## name `NA` Positive `Value -999999` Negative Total
## <chr> <int> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_~ 6229 352869 645 0 359743
## 2 all_adult_hospital_inpatient_b~ 3313 327037 29385 8 359743
## 3 icu_beds_used_7_day_avg 1644 314605 43487 7 359743
## 4 inpatient_beds_7_day_avg 1722 356603 1418 0 359743
## 5 staffed_icu_adult_patients_con~ 4243 249411 106089 0 359743
## 6 total_adult_patients_hospitali~ 2364 249154 108225 0 359743
## 7 total_beds_7_day_avg 1294 358116 333 0 359743
## 8 total_icu_beds_7_day_avg 2064 339859 17820 0 359743## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## # A tibble: 359,743 x 106
## hospital_pk collection_week state ccn hospital_name address city zip
## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 670031 2021-12-10 TX 670031 ST LUKE'S PATI~ 4600 EA~ PASA~ 77505
## 2 061314 2020-07-31 CO 061314 MEMORIAL HOSPI~ 750 HOS~ CRAIG 81625
## 3 520034 2020-07-31 WI 520034 AURORA MEDICAL~ 5000 ME~ TWO ~ 54241
## 4 522008 2020-07-31 WI 522008 SELECT SPECIAL~ 801 BRA~ MADI~ 53715
## 5 050769 2020-07-31 CA 050769 HOAG ORTHOPEDI~ 16250 S~ IRVI~ 92618
## 6 501329 2020-08-07 WA 501329 PEACEHEALTH UN~ 2000 HO~ SEDR~ 98284
## 7 280134 2020-07-31 NE 280134 KEARNEY REGION~ 804 22N~ KEAR~ 68845
## 8 452034 2020-07-31 TX 452034 CORNERSTONE SP~ 4207 BU~ AUST~ 78756
## 9 330229 2020-07-31 NY 330229 BROOKS-TLC HOS~ 529 CEN~ DUNK~ 14048
## 10 451341 2020-08-07 TX 451341 HASKELL MEMORI~ 1 NORTH~ HASK~ 79521
## # ... with 359,733 more rows, and 98 more variables: hospital_subtype <chr>,
## # fips_code <chr>, is_metro_micro <lgl>, total_beds_7_day_avg <dbl>,
## # all_adult_hospital_beds_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_beds_7_day_avg <dbl>,
## # inpatient_beds_used_7_day_avg <dbl>,
## # all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl>,
## # total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl>,
## # total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl>,
## # inpatient_beds_7_day_avg <dbl>, total_icu_beds_7_day_avg <dbl>,
## # total_staffed_adult_icu_beds_7_day_avg <dbl>,
## # icu_beds_used_7_day_avg <dbl>,
## # staffed_adult_icu_bed_occupancy_7_day_avg <dbl>,
## # staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl>,
## # staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl>,
## # total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl>,
## # icu_patients_confirmed_influenza_7_day_avg <dbl>,
## # total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl>,
## # total_beds_7_day_sum <dbl>, all_adult_hospital_beds_7_day_sum <dbl>,
## # all_adult_hospital_inpatient_beds_7_day_sum <dbl>,
## # inpatient_beds_used_7_day_sum <dbl>,
## # all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl>,
## # total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl>,
## # total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl>,
## # inpatient_beds_7_day_sum <dbl>, total_icu_beds_7_day_sum <dbl>,
## # total_staffed_adult_icu_beds_7_day_sum <dbl>,
## # icu_beds_used_7_day_sum <dbl>,
## # staffed_adult_icu_bed_occupancy_7_day_sum <dbl>,
## # staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl>,
## # staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl>,
## # total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl>,
## # icu_patients_confirmed_influenza_7_day_sum <dbl>,
## # total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl>,
## # total_beds_7_day_coverage <dbl>,
## # all_adult_hospital_beds_7_day_coverage <dbl>,
## # all_adult_hospital_inpatient_beds_7_day_coverage <dbl>,
## # inpatient_beds_used_7_day_coverage <dbl>,
## # all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl>,
## # total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl>,
## # total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl>,
## # total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl>,
## # inpatient_beds_7_day_coverage <dbl>, total_icu_beds_7_day_coverage <dbl>,
## # total_staffed_adult_icu_beds_7_day_coverage <dbl>,
## # icu_beds_used_7_day_coverage <dbl>,
## # staffed_adult_icu_bed_occupancy_7_day_coverage <dbl>,
## # staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl>,
## # staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl>,
## # total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl>,
## # icu_patients_confirmed_influenza_7_day_coverage <dbl>,
## # total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl>,
## # previous_day_admission_adult_covid_confirmed_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_18-19_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_20-29_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_30-39_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_40-49_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_50-59_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_60-69_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_70-79_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_80+_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl>,
## # previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl>,
## # previous_day_covid_ED_visits_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_18-19_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_20-29_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_30-39_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_40-49_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_50-59_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_60-69_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_70-79_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_80+_7_day_sum <dbl>,
## # previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl>,
## # previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl>,
## # previous_day_total_ED_visits_7_day_sum <dbl>,
## # previous_day_admission_influenza_confirmed_7_day_sum <dbl>,
## # geocoded_hospital_address <chr>, hhs_ids <chr>,
## # previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl>,
## # previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl>,
## # previous_day_admission_adult_covid_suspected_7_day_coverage <dbl>,
## # previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl>,
## # previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl>,
## # total_personnel_covid_vaccinated_doses_none_7_day <dbl>,
## # total_personnel_covid_vaccinated_doses_one_7_day <dbl>,
## # total_personnel_covid_vaccinated_doses_all_7_day <dbl>,
## # previous_week_patients_covid_vaccinated_doses_one_7_day <dbl>,
## # previous_week_patients_covid_vaccinated_doses_all_7_day <dbl>,
## # is_corrected <lgl># Run for latest data, save as RDS
indivHosp_20211231 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20211231.csv")##
## File ./RInputFiles/Coronavirus/HHS_Hospital_20211231.csv already exists
## File will not be downloaded since ovrWrite is not TRUE##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 364,751
## Columns: 106
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 6850
## 2 Critical Access Hospitals 97451
## 3 Long Term 25067
## 4 Short Term 235383
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 18
## 2 GU 146
## 3 MP 73
## 4 PR 4029
## 5 VI 146
##
## Record types for key metrics
## # A tibble: 8 x 6
## name `NA` Positive `Value -999999` Negative Total
## <chr> <int> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_~ 6229 357867 655 0 364751
## 2 all_adult_hospital_inpatient_b~ 3313 331689 29741 8 364751
## 3 icu_beds_used_7_day_avg 1644 319076 44024 7 364751
## 4 inpatient_beds_7_day_avg 1722 361591 1438 0 364751
## 5 staffed_icu_adult_patients_con~ 4243 253046 107462 0 364751
## 6 total_adult_patients_hospitali~ 2364 252709 109678 0 364751
## 7 total_beds_7_day_avg 1294 363119 338 0 364751
## 8 total_icu_beds_7_day_avg 2064 344610 18077 0 364751## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20211231)Updated plots can be produced:
# Example hospital plots for a single state-level segment
indivHosp_20211231 %>%
filter(state %in% c(names(cdc_daily_211224$useClusters[cdc_daily_211224$useClusters==7])),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20211225, keyHosp=., plotTitle="State Segment 7 Hospitals Summed")
The missing data for “adult_beds_occupied” and trend break for “adult_icu_covid” should be explored further. The process is updated to run for each state-level segment:
purrr::walk(.x=sort(unique(cdc_daily_211224$useClusters)),
.f=function(segNum) {
indivHosp_20211231 %>%
filter(state %in% c(names(cdc_daily_211224$useClusters[cdc_daily_211224$useClusters==segNum])),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20211225,
keyHosp=.,
plotTitle=paste0(paste0("State Segment ", segNum, " Hospitals Summed"))
)
}
)
The latest data are downloaded and processed:
readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220103.csv",
"cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220103.csv",
"vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220103.csv"
)
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_211224")$dfRaw$cdcDaily,
"cdcHosp"=readFromRDS("cdc_daily_211224")$dfRaw$cdcHosp,
"vax"=readFromRDS("cdc_daily_211224")$dfRaw$vax
)
cdc_daily_220103 <- readRunCDCDaily(thruLabel="Jan 02, 2022",
downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x),
readFrom=readList,
compareFile=compareList,
writeLog=NULL,
useClusters=readFromRDS("cdc_daily_210528")$useClusters,
weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7",
"vxcpm7", "vxcgte65pct"
),
skipAssessmentPlots=FALSE,
brewPalette="Paired"
)##
## -- Column specification --------------------------------------------------------
## cols(
## submission_date = col_character(),
## state = col_character(),
## tot_cases = col_double(),
## conf_cases = col_double(),
## prob_cases = col_double(),
## new_case = col_double(),
## pnew_case = col_double(),
## tot_death = col_double(),
## conf_death = col_double(),
## prob_death = col_double(),
## new_death = col_double(),
## pnew_death = col_double(),
## created_at = col_character(),
## consent_cases = col_character(),
## consent_deaths = col_character()
## )##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 9
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## date name newValue refValue absDelta pctDelta
## 1 2021-12-19 new_deaths 194 167 27 0.14958449
## 2 2021-12-18 new_deaths 508 472 36 0.07346939
## 3 2021-12-12 new_deaths 258 242 16 0.06400000
## 4 2021-12-18 new_cases 82555 76510 6045 0.07600666
## 5 2021-12-19 new_cases 99052 94190 4862 0.05032032
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC new_deaths 19295 19233 62 0.003218439
## 2 AL new_deaths 16451 16402 49 0.002982985
## 3 FL new_deaths 62454 62347 107 0.001714730
## 4 NC new_cases 1610503 1607378 3125 0.001942272
## 5 RI new_cases 215918 215541 377 0.001747559
## 6 AL new_cases 863302 862331 971 0.001125384
##
##
##
## Raw file for cdcDaily:
## Rows: 42,600
## Columns: 15
## $ date <date> 2021-04-01, 2021-05-31, 2020-02-06, 2020-07-30, 2021-0~
## $ state <chr> "CA", "CA", "NE", "ME", "MS", "NH", "ND", "GU", "NC", "~
## $ tot_cases <dbl> 3570660, 3685032, 0, 3910, 280182, 2518, 6602, 449, 274~
## $ conf_cases <dbl> 3570660, 3685032, NA, 3497, 176228, NA, 6602, NA, 26208~
## $ prob_cases <dbl> 0, 0, NA, 413, 103954, NA, 0, NA, 12226, NA, 219, NA, 1~
## $ new_cases <dbl> 2234, 644, 0, 22, 1059, 89, 133, 15, 2333, 798, 386, 0,~
## $ pnew_case <dbl> 0, 0, NA, 2, 559, 0, 0, 0, 244, 0, 5, NA, 0, 94, 45, 0,~
## $ tot_deaths <dbl> 58090, 62011, 0, 123, 6730, 86, 103, 5, 4731, 4169, 681~
## $ conf_death <dbl> 58090, 62011, NA, 122, 4739, NA, NA, NA, 4623, 4012, 63~
## $ prob_death <dbl> 0, 0, NA, 1, 1991, NA, NA, NA, 108, 157, 42, NA, 1332, ~
## $ new_deaths <dbl> 154, 5, 0, 2, 13, 2, 0, 0, 34, 7, 16, 0, 0, 8, 0, 33, 0~
## $ pnew_death <dbl> 0, 0, NA, 0, 7, 0, 0, 0, 2, 0, 2, NA, 0, 0, 0, 0, NA, 8~
## $ created_at <chr> "04/03/2021 12:00:00 AM", "06/02/2021 12:00:00 AM", "03~
## $ consent_cases <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Not agree~
## $ consent_deaths <chr> "Agree", "Agree", "Agree", "Agree", "Agree", "Not agree~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## state = col_character(),
## date = col_date(format = ""),
## geocoded_state = col_logical()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 10
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 5 and at least 5%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
## ***Differences of at least 0 and at least 0.1%
##
## state name newValue refValue absDelta pctDelta
## 1 NC inp 1001010 997299 3711 0.003714140
## 2 NH hosp_ped 579 537 42 0.075268817
## 3 KS hosp_ped 2894 3085 191 0.063890283
## 4 MD hosp_ped 9405 9733 328 0.034277354
## 5 NJ hosp_ped 12257 12016 241 0.019857455
## 6 WV hosp_ped 3592 3663 71 0.019572708
## 7 VA hosp_ped 10634 10456 178 0.016880038
## 8 DE hosp_ped 2949 2909 40 0.013656538
## 9 NV hosp_ped 3170 3133 37 0.011740441
## 10 AZ hosp_ped 18483 18664 181 0.009745067
## 11 AL hosp_ped 13741 13622 119 0.008697877
## 12 KY hosp_ped 12337 12429 92 0.007429540
## 13 MA hosp_ped 6703 6658 45 0.006736023
## 14 SC hosp_ped 5397 5432 35 0.006464124
## 15 UT hosp_ped 5232 5265 33 0.006287511
## 16 AR hosp_ped 8608 8555 53 0.006176076
## 17 TN hosp_ped 13309 13384 75 0.005619451
## 18 CT hosp_ped 3233 3251 18 0.005552128
## 19 VT hosp_ped 210 211 1 0.004750594
## 20 SD hosp_ped 3155 3166 11 0.003480462
## 21 NC hosp_ped 17837 17778 59 0.003313211
## 22 CO hosp_ped 14704 14749 45 0.003055716
## 23 OK hosp_ped 17313 17271 42 0.002428869
## 24 NM hosp_ped 4955 4943 12 0.002424732
## 25 RI hosp_ped 2060 2065 5 0.002424242
## 26 NY hosp_ped 44591 44484 107 0.002402470
## 27 IL hosp_ped 28075 28009 66 0.002353612
## 28 ME hosp_ped 885 887 2 0.002257336
## 29 MS hosp_ped 7448 7463 15 0.002011937
## 30 PR hosp_ped 14354 14382 28 0.001948775
## 31 PA hosp_ped 34007 33952 55 0.001618623
## 32 HI hosp_ped 1483 1481 2 0.001349528
## 33 GA hosp_ped 34629 34583 46 0.001329249
## 34 ND hosp_ped 2320 2323 3 0.001292268
## 35 CA hosp_ped 45739 45684 55 0.001203198
## 36 WI hosp_ped 6916 6909 7 0.001012658
##
##
##
## Raw file for cdcHosp:
## Rows: 36,028
## Columns: 117
## $ state <chr> ~
## $ date <date> ~
## $ critical_staffing_shortage_today_yes <dbl> ~
## $ critical_staffing_shortage_today_no <dbl> ~
## $ critical_staffing_shortage_today_not_reported <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_yes <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_no <dbl> ~
## $ critical_staffing_shortage_anticipated_within_week_not_reported <dbl> ~
## $ hospital_onset_covid <dbl> ~
## $ hospital_onset_covid_coverage <dbl> ~
## $ inpatient_beds <dbl> ~
## $ inpatient_beds_coverage <dbl> ~
## $ inpatient_beds_used <dbl> ~
## $ inpatient_beds_used_coverage <dbl> ~
## $ inp <dbl> ~
## $ inpatient_beds_used_covid_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected <dbl> ~
## $ previous_day_admission_adult_covid_suspected_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy <dbl> ~
## $ staffed_adult_icu_bed_occupancy_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_coverage <dbl> ~
## $ hosp_adult <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ hosp_ped <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage <dbl> ~
## $ total_staffed_adult_icu_beds <dbl> ~
## $ total_staffed_adult_icu_beds_coverage <dbl> ~
## $ inpatient_beds_utilization <dbl> ~
## $ inpatient_beds_utilization_coverage <dbl> ~
## $ inpatient_beds_utilization_numerator <dbl> ~
## $ inpatient_beds_utilization_denominator <dbl> ~
## $ percent_of_inpatients_with_covid <dbl> ~
## $ percent_of_inpatients_with_covid_coverage <dbl> ~
## $ percent_of_inpatients_with_covid_numerator <dbl> ~
## $ percent_of_inpatients_with_covid_denominator <dbl> ~
## $ inpatient_bed_covid_utilization <dbl> ~
## $ inpatient_bed_covid_utilization_coverage <dbl> ~
## $ inpatient_bed_covid_utilization_numerator <dbl> ~
## $ inpatient_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_covid_utilization <dbl> ~
## $ adult_icu_bed_covid_utilization_coverage <dbl> ~
## $ adult_icu_bed_covid_utilization_numerator <dbl> ~
## $ adult_icu_bed_covid_utilization_denominator <dbl> ~
## $ adult_icu_bed_utilization <dbl> ~
## $ adult_icu_bed_utilization_coverage <dbl> ~
## $ adult_icu_bed_utilization_numerator <dbl> ~
## $ adult_icu_bed_utilization_denominator <dbl> ~
## $ geocoded_state <lgl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_coverage` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_coverage <dbl> ~
## $ deaths_covid <dbl> ~
## $ deaths_covid_coverage <dbl> ~
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses <dbl> ~
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses <dbl> ~
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used <dbl> ~
## $ previous_week_therapeutic_b_bamlanivimab_courses_used <dbl> ~
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used <dbl> ~
## $ icu_patients_confirmed_influenza <dbl> ~
## $ icu_patients_confirmed_influenza_coverage <dbl> ~
## $ previous_day_admission_influenza_confirmed <dbl> ~
## $ previous_day_admission_influenza_confirmed_coverage <dbl> ~
## $ previous_day_deaths_covid_and_influenza <dbl> ~
## $ previous_day_deaths_covid_and_influenza_coverage <dbl> ~
## $ previous_day_deaths_influenza <dbl> ~
## $ previous_day_deaths_influenza_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_coverage <dbl> ~##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Date = col_character(),
## Location = col_character()
## )
## i Use `spec()` for the full column specifications.
##
## *** File has been checked for uniqueness by: state date
##
##
## Checking for similarity of: column names
## In reference but not in current:
## In current but not in reference:
##
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 7
##
## Checking for similarity of: state
## In reference but not in current:
## In current but not in reference:
##
##
## ***Differences of at least 1 and at least 1%
##
## [1] date name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)##
##
## ***Differences of at least 0 and at least 0.1%
##
## [1] state name newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
##
##
##
## Raw file for vax:
## Rows: 24,728
## Columns: 80
## $ date <date> 2021-12-30, 2021-12-30, 2021-1~
## $ MMWR_week <dbl> 52, 52, 52, 52, 52, 52, 52, 52,~
## $ state <chr> "WV", "NY", "ME", "KS", "WI", "~
## $ Distributed <dbl> 3344485, 39052505, 2908420, 517~
## $ Distributed_Janssen <dbl> 156400, 1789500, 150600, 249000~
## $ Distributed_Moderna <dbl> 1165240, 13435720, 1120600, 195~
## $ Distributed_Pfizer <dbl> 2022845, 23827285, 1637220, 297~
## $ Distributed_Unk_Manuf <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Dist_Per_100K <dbl> 186619, 200747, 216366, 177730,~
## $ Distributed_Per_100k_12Plus <dbl> 214688, 232827, 245450, 211194,~
## $ Distributed_Per_100k_18Plus <dbl> 233459, 253172, 265519, 233966,~
## $ Distributed_Per_100k_65Plus <dbl> 911277, 1184790, 1019550, 10889~
## $ vxa <dbl> 2490508, 34256514, 2508118, 407~
## $ Administered_12Plus <dbl> 2457972, 33507499, 2440464, 398~
## $ Administered_18Plus <dbl> 2346955, 31564214, 2314179, 373~
## $ Administered_65Plus <dbl> 828621, 7938556, 774178, 114864~
## $ Administered_Janssen <dbl> 63838, 1344059, 138615, 128180,~
## $ Administered_Moderna <dbl> 1080327, 12566165, 1026213, 157~
## $ Administered_Pfizer <dbl> 1344726, 20330689, 1340722, 236~
## $ Administered_Unk_Manuf <dbl> 1617, 15601, 2568, 3354, 1790, ~
## $ Admin_Per_100k <dbl> 138968, 176094, 186586, 139833,~
## $ Admin_Per_100k_12Plus <dbl> 157781, 199768, 205957, 162434,~
## $ Admin_Per_100k_18Plus <dbl> 163827, 204627, 211269, 168573,~
## $ Admin_Per_100k_65Plus <dbl> 225776, 240844, 271389, 241573,~
## $ Recip_Administered <dbl> 2481826, 34257536, 2519706, 417~
## $ Administered_Dose1_Recip <dbl> 1109524, 16343955, 1154119, 201~
## $ Administered_Dose1_Pop_Pct <dbl> 61.9, 84.0, 85.9, 69.3, 68.2, 6~
## $ Administered_Dose1_Recip_12Plus <dbl> 1089308, 15876430, 1116022, 196~
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 69.9, 94.7, 94.2, 80.0, 77.0, 7~
## $ Administered_Dose1_Recip_18Plus <dbl> 1031604, 14856332, 1052121, 182~
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 72.0, 95.0, 95.0, 82.6, 79.1, 7~
## $ Administered_Dose1_Recip_65Plus <dbl> 333931, 3374517, 319008, 514743~
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 91.0, 95.0, 95.0, 95.0, 95.0, 9~
## $ vxc <dbl> 986688, 13961789, 1019209, 1661~
## $ vxcpoppct <dbl> 55.1, 71.8, 75.8, 57.0, 62.0, 5~
## $ Series_Complete_12Plus <dbl> 974298, 13678603, 989971, 16266~
## $ Series_Complete_12PlusPop_Pct <dbl> 62.5, 81.6, 83.5, 66.3, 70.4, 6~
## $ vxcgte18 <dbl> 923854, 12786149, 931969, 15142~
## $ vxcgte18pct <dbl> 64.5, 82.9, 85.1, 68.4, 72.5, 6~
## $ vxcgte65 <dbl> 304047, 2925706, 278312, 419367~
## $ vxcgte65pct <dbl> 82.8, 88.8, 95.0, 88.2, 93.5, 8~
## $ Series_Complete_Janssen <dbl> 58481, 1203587, 128481, 117897,~
## $ Series_Complete_Moderna <dbl> 409874, 4800641, 371443, 606491~
## $ Series_Complete_Pfizer <dbl> 517891, 7953437, 518728, 936172~
## $ Series_Complete_Unk_Manuf <dbl> 442, 4124, 557, 610, 592, 4, 21~
## $ Series_Complete_Janssen_12Plus <dbl> 58464, 1203406, 128459, 117877,~
## $ Series_Complete_Moderna_12Plus <dbl> 409783, 4800070, 371424, 606475~
## $ Series_Complete_Pfizer_12Plus <dbl> 505611, 7671070, 489532, 901724~
## $ Series_Complete_Unk_Manuf_12Plus <dbl> 440, 4057, 556, 606, 591, 4, 21~
## $ Series_Complete_Janssen_18Plus <dbl> 58409, 1202630, 128403, 117769,~
## $ Series_Complete_Moderna_18Plus <dbl> 409423, 4798084, 371366, 606123~
## $ Series_Complete_Pfizer_18Plus <dbl> 455611, 6781663, 431691, 789846~
## $ Series_Complete_Unk_Manuf_18Plus <dbl> 411, 3772, 509, 531, 565, 4, 20~
## $ Series_Complete_Janssen_65Plus <dbl> 9431, 183872, 24414, 18152, 300~
## $ Series_Complete_Moderna_65Plus <dbl> 155599, 1381920, 126724, 199682~
## $ Series_Complete_Pfizer_65Plus <dbl> 138826, 1358310, 126945, 201282~
## $ Series_Complete_Unk_Manuf_65Plus <dbl> 191, 1604, 229, 251, 253, 1, 75~
## $ Additional_Doses <dbl> 369484, 4543102, 448113, 554996~
## $ Additional_Doses_Vax_Pct <dbl> 37.4, 32.5, 44.0, 33.4, 44.4, 3~
## $ Additional_Doses_18Plus <dbl> 367744, 4510445, 444887, 550751~
## $ Additional_Doses_18Plus_Vax_Pct <dbl> 39.8, 35.3, 47.7, 36.4, 48.2, 3~
## $ Additional_Doses_50Plus <dbl> 282480, 3008478, 325651, 390779~
## $ Additional_Doses_50Plus_Vax_Pct <dbl> 50.3, 47.6, 59.9, 48.1, 60.9, 5~
## $ Additional_Doses_65Plus <dbl> 180399, 1695530, 194176, 243080~
## $ Additional_Doses_65Plus_Vax_Pct <dbl> 59.3, 58.0, 69.8, 58.0, 72.3, 6~
## $ Additional_Doses_Moderna <dbl> 176516, 2019212, 212290, 248509~
## $ Additional_Doses_Pfizer <dbl> 189144, 2444709, 226615, 298262~
## $ Additional_Doses_Janssen <dbl> 3769, 78940, 8913, 8101, 22104,~
## $ Additional_Doses_Unk_Manuf <dbl> 55, 241, 295, 124, 55, 3, 211, ~
## $ Administered_Dose1_Recip_5Plus <dbl> 1109130, 16342423, 1154000, 201~
## $ Administered_Dose1_Recip_5PlusPop_Pct <dbl> 65.3, 89.2, 90.1, 74.0, 72.3, 6~
## $ Series_Complete_5Plus <dbl> 986456, 13961401, 1019177, 1661~
## $ Series_Complete_5PlusPop_Pct <dbl> 58.1, 76.2, 79.6, 60.9, 65.7, 5~
## $ Administered_5Plus <dbl> 2489811, 34254491, 2507987, 407~
## $ Admin_Per_100k_5Plus <dbl> 146535, 186912, 195833, 149328,~
## $ Distributed_Per_100k_5Plus <dbl> 196836, 213092, 227101, 189804,~
## $ Series_Complete_Moderna_5Plus <dbl> 409797, 4800525, 371436, 606483~
## $ Series_Complete_Pfizer_5Plus <dbl> 517751, 7953284, 518722, 936166~
## $ Series_Complete_Janssen_5Plus <dbl> 58466, 1203483, 128462, 117892,~
## $ Series_Complete_Unk_Manuf_5Plus <dbl> 442, 4109, 557, 610, 592, 4, 21~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 6
## isType tot_cases tot_deaths new_cases new_deaths n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 1.50e+10 2.74e+8 5.44e+7 803968 41890
## 2 after 1.49e+10 2.73e+8 5.41e+7 800305 36210
## 3 pctchg 4.39e- 3 4.26e-3 5.60e-3 0.00456 0.136
##
##
## Processed for cdcDaily:
## Rows: 36,210
## Columns: 6
## $ date <date> 2021-04-01, 2021-05-31, 2020-02-06, 2020-07-30, 2021-02-02~
## $ state <chr> "CA", "CA", "NE", "ME", "MS", "NH", "ND", "NC", "MD", "AL",~
## $ tot_cases <dbl> 3570660, 3685032, 0, 3910, 280182, 2518, 6602, 274314, 1351~
## $ tot_deaths <dbl> 58090, 62011, 0, 123, 6730, 86, 103, 4731, 4169, 681, 0, 73~
## $ new_cases <dbl> 2234, 644, 0, 22, 1059, 89, 133, 2333, 798, 386, 0, 0, 1502~
## $ new_deaths <dbl> 154, 5, 0, 2, 13, 2, 0, 34, 7, 16, 0, 0, 8, 0, 33, 0, 32, 6~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 5
## isType inp hosp_adult hosp_ped n
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 before 3.90e+7 3.29e+7 749511 36028
## 2 after 3.88e+7 3.27e+7 734450 34584
## 3 pctchg 4.85e-3 4.65e-3 0.0201 0.0401
##
##
## Processed for cdcHosp:
## Rows: 34,584
## Columns: 5
## $ date <date> 2020-10-18, 2020-10-15, 2020-10-14, 2020-10-13, 2020-10-13~
## $ state <chr> "VT", "AK", "RI", "AR", "NH", "NC", "HI", "ND", "ID", "NH",~
## $ inp <dbl> 2, 59, 128, 732, 34, 1329, 110, 255, 191, 46, 420, 91, 42, ~
## $ hosp_adult <dbl> 2, 58, 128, 710, 34, 1311, 108, 246, 189, 46, 417, 90, 42, ~
## $ hosp_ped <dbl> 0, 1, 0, 11, 0, 17, 2, 9, 2, 0, 3, 1, 0, 6, 2, 1, 4, 1, 15,~
##
## Column sums before and after applying filtering rules:
## # A tibble: 3 x 9
## isType vxa vxc vxcpoppct vxcgte65 vxcgte65pct vxcgte18 vxcgte18pct
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 before 2.10e+11 9.12e+10 810774. 2.53e+10 1294323 8.66e+10 978759.
## 2 after 1.01e+11 4.41e+10 681735. 1.22e+10 1166666. 4.18e+10 832309.
## 3 pctchg 5.21e- 1 5.17e- 1 0.159 5.16e- 1 0.0986 5.17e- 1 0.150
## # ... with 1 more variable: n <dbl>
##
##
## Processed for vax:
## Rows: 19,482
## Columns: 9
## $ date <date> 2021-12-30, 2021-12-30, 2021-12-30, 2021-12-30, 2021-12-3~
## $ state <chr> "WV", "NY", "ME", "KS", "WI", "ND", "CO", "DC", "MO", "OR"~
## $ vxa <dbl> 2490508, 34256514, 2508118, 4073766, 9114543, 1013290, 951~
## $ vxc <dbl> 986688, 13961789, 1019209, 1661170, 3607515, 400490, 38135~
## $ vxcpoppct <dbl> 55.1, 71.8, 75.8, 57.0, 62.0, 52.6, 66.2, 67.6, 53.0, 66.5~
## $ vxcgte65 <dbl> 304047, 2925706, 278312, 419367, 950813, 101294, 751171, 7~
## $ vxcgte65pct <dbl> 82.8, 88.8, 95.0, 88.2, 93.5, 84.5, 89.2, 88.8, 82.4, 89.2~
## $ vxcgte18 <dbl> 923854, 12786149, 931969, 1514269, 3301306, 370742, 344120~
## $ vxcgte18pct <dbl> 64.5, 82.9, 85.1, 68.4, 72.5, 63.7, 76.5, 77.3, 63.0, 76.3~
##
## Integrated per capita data file:
## Rows: 36,474
## Columns: 34
## $ date <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0~
## $ state <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"~
## $ tot_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tot_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_cases <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ inp <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, ~
## $ hosp_adult <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hosp_ped <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxc <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpoppct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA~
## $ ahpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ tdpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ cpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ dpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ hpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ ahpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ phpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxapm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ vxcpm7 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in CRS definition
saveToRDS(cdc_daily_220103)The latest hospital data are downloaded:
# Run for latest data, save as RDS
indivHosp_20220109 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220109.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## hospital_pk = col_character(),
## collection_week = col_date(format = ""),
## state = col_character(),
## ccn = col_character(),
## hospital_name = col_character(),
## address = col_character(),
## city = col_character(),
## zip = col_character(),
## hospital_subtype = col_character(),
## fips_code = col_character(),
## is_metro_micro = col_logical(),
## geocoded_hospital_address = col_character(),
## hhs_ids = col_character(),
## is_corrected = col_logical()
## )
## i Use `spec()` for the full column specifications.## Rows: 369,758
## Columns: 106
## $ hospital_pk <chr> ~
## $ collection_week <date> ~
## $ state <chr> ~
## $ ccn <chr> ~
## $ hospital_name <chr> ~
## $ address <chr> ~
## $ city <chr> ~
## $ zip <chr> ~
## $ hospital_subtype <chr> ~
## $ fips_code <chr> ~
## $ is_metro_micro <lgl> ~
## $ total_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_beds_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_avg <dbl> ~
## $ inpatient_beds_used_7_day_avg <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg <dbl> ~
## $ inpatient_beds_7_day_avg <dbl> ~
## $ total_icu_beds_7_day_avg <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_avg <dbl> ~
## $ icu_beds_used_7_day_avg <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_avg <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg <dbl> ~
## $ total_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_beds_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_sum <dbl> ~
## $ inpatient_beds_used_7_day_sum <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum <dbl> ~
## $ inpatient_beds_7_day_sum <dbl> ~
## $ total_icu_beds_7_day_sum <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_sum <dbl> ~
## $ icu_beds_used_7_day_sum <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_sum <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum <dbl> ~
## $ total_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_beds_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_beds_7_day_coverage <dbl> ~
## $ inpatient_beds_used_7_day_coverage <dbl> ~
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage <dbl> ~
## $ inpatient_beds_7_day_coverage <dbl> ~
## $ total_icu_beds_7_day_coverage <dbl> ~
## $ total_staffed_adult_icu_beds_7_day_coverage <dbl> ~
## $ icu_beds_used_7_day_coverage <dbl> ~
## $ staffed_adult_icu_bed_occupancy_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage <dbl> ~
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage <dbl> ~
## $ icu_patients_confirmed_influenza_7_day_coverage <dbl> ~
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum <dbl> ~
## $ previous_day_covid_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_sum <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum` <dbl> ~
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum` <dbl> ~
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum <dbl> ~
## $ previous_day_total_ED_visits_7_day_sum <dbl> ~
## $ previous_day_admission_influenza_confirmed_7_day_sum <dbl> ~
## $ geocoded_hospital_address <chr> ~
## $ hhs_ids <chr> ~
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage <dbl> ~
## $ previous_day_admission_adult_covid_suspected_7_day_coverage <dbl> ~
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage <dbl> ~
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_none_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_one_7_day <dbl> ~
## $ total_personnel_covid_vaccinated_doses_all_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_one_7_day <dbl> ~
## $ previous_week_patients_covid_vaccinated_doses_all_7_day <dbl> ~
## $ is_corrected <lgl> ~
##
## Hospital Subtype Counts:
## # A tibble: 4 x 2
## hospital_subtype n
## <chr> <int>
## 1 Childrens Hospitals 6943
## 2 Critical Access Hospitals 98792
## 3 Long Term 25409
## 4 Short Term 238614
##
## Records other than 50 states and DC
## # A tibble: 5 x 2
## state n
## <chr> <int>
## 1 AS 19
## 2 GU 148
## 3 MP 74
## 4 PR 4082
## 5 VI 148
##
## Record types for key metrics
## # A tibble: 8 x 6
## name `NA` Positive `Value -999999` Negative Total
## <chr> <int> <int> <int> <int> <int>
## 1 all_adult_hospital_beds_7_day_~ 6229 362863 666 0 369758
## 2 all_adult_hospital_inpatient_b~ 3313 336307 30130 8 369758
## 3 icu_beds_used_7_day_avg 1644 323534 44573 7 369758
## 4 inpatient_beds_7_day_avg 1722 366579 1457 0 369758
## 5 staffed_icu_adult_patients_con~ 4243 256690 108825 0 369758
## 6 total_adult_patients_hospitali~ 2364 256272 111122 0 369758
## 7 total_beds_7_day_avg 1294 368119 345 0 369758
## 8 total_icu_beds_7_day_avg 2064 349349 18345 0 369758## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
saveToRDS(indivHosp_20220109)The process is updated to run for each state-level segment:
purrr::walk(.x=sort(unique(cdc_daily_220103$useClusters)),
.f=function(segNum) {
indivHosp_20220109 %>%
filter(state %in% c(names(cdc_daily_220103$useClusters[cdc_daily_220103$useClusters==segNum])),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20220109,
keyHosp=.,
plotTitle=paste0(paste0("State Segment ", segNum, " Hospitals Summed"))
)
}
)
A summary is also created for all US hospitals:
indivHosp_20220109 %>%
filter(state %in% c(state.abb, "DC"),
collection_week==max(collection_week)
) %>%
pull(hospital_pk) %>%
plotHospitalUtilization(df=indivHosp_20220109, keyHosp=., plotTitle="US Hospitals Summed")
Maps are created for the ICU utilization by state:
stateHosp_20220109 <- indivHosp_20220109 %>%
filter(state %in% c(state.abb, "DC")) %>%
colSelector(c("state", "collection_week", names(hhsMapper))) %>%
colRenamer(hhsMapper) %>%
mutate(across(where(is.numeric), .fns=function(x) ifelse(is.na(x), NA, ifelse(x==-999999, NA, x)))) %>%
group_by(state, collection_week) %>%
summarize(across(where(is.numeric), .fns=sum, na.rm=TRUE), n=n(),.groups="drop")
library(geofacet)## Warning: package 'geofacet' was built under R version 4.1.2stateHosp_20220109 %>%
mutate(pctICU=icu_beds_occupied/icu_beds, pctCovidICU=adult_icu_covid/icu_beds) %>%
ggplot(aes(x=collection_week)) +
geom_line(aes(y=pctICU, color="Total")) +
geom_line(aes(y=pctCovidICU, color="Covid")) +
scale_color_manual("% ICU\nUsed", values=c("Total"="black", "Covid"="red")) +
labs(x=NULL,
y=NULL,
title="Average % ICU Capacity Filled by Week",
subtitle="August 2020 to December 2021"
) +
theme(axis.text.x = element_blank()) +
geom_hline(yintercept=1, lty=2) +
facet_geo(~state)
The process is converted to functional form:
createGeoMap <- function(df,
yVars,
xVar="collection_week",
facetVar="state",
lstFilter=list(),
lstExclude=list(),
vecSelect=NULL,
vecRename=c(),
selfList=list(),
fullList=list(),
plotTitle=NULL,
plotSubtitle=NULL,
plotYLab=NULL,
plotScaleLabel=NULL,
createPlot=TRUE,
returnData=FALSE
) {
# FUNCTION ARGUMENTS:
# df: the data frame containing the relevant data
# yVars: list of the y-variables, of form list("yVar1"=c("label"="y1Label", "color"="y1Color"), "yVar2"=...)
# xVar: the x-variable to use for the plots
# facetVar: the variable for faceting the data
# lstFilter: a list for filtering records, of form list("field"=c("allowed values"))
# lstExclude: a list for filtering records, of form list("field"=c("disallowed values"))
# vecSelect: vector for variables to keep c('keep1', "keep2", ...), NULL means keep all
# vecRename: vector for renaming c('existing name'='new name'), can be any length from 0 to ncol(df)
# selfList: list for functions to apply to self, list('variable'=fn) will apply variable=fn(variable)
# processed in order, so more than one function can be applied to self
# fullList: list for general functions to be applied, list('new variable'=expression(code))
# will create 'new variable' as eval(expression(code))
# for now, requires passing an expression
# plotTitle: title for plot
# plotSubtitle: subtitle for plot
# plotYLab: y-label for plot
# plotScaleLabel: scale label for plot
# createPlot: boolean, should the plot be created and printed?
# returnData: boolean, should the data frame dfMod be returned?
# Create the modified data
dfMod <- df %>%
rowFilter(lstFilter=lstFilter, lstExclude=lstExclude) %>%
colSelector(vecSelect=vecSelect) %>%
colRenamer(vecRename=vecRename) %>%
colMutater(selfList=selfList, fullList=fullList)
if(isTRUE(createPlot)) {
# Create the plot data frame
dfPlot <- dfMod %>%
colSelector(c(facetVar, xVar, names(yVars))) %>%
pivot_longer(names(yVars))
# Create the color mapper
vecColor <- sapply(yVars, FUN=function(x) x[["color"]]) %>%
purrr::set_names(names(yVars))
# Create the plot
p1 <- dfPlot %>%
colRenamer(c("facetVar") %>% purrr::set_names(facetVar)) %>%
ggplot(aes_string(x=xVar, y="value", group="name", color="name")) +
geom_line() +
scale_color_manual(plotScaleLabel,
values=vecColor,
labels=sapply(yVars, FUN=function(x) x[["label"]])
) +
labs(x=NULL, y=plotYLab, title=plotTitle, subtitle=plotSubtitle) +
theme(axis.text.x = element_blank()) +
geom_hline(yintercept=1, lty=2) +
facet_geo(~facetVar)
# Print the plot
print(p1)
}
# Return the data if requested(?)
if(isTRUE(returnData)) return(dfMod)
}
# ICU summary
createGeoMap(stateHosp_20220109,
yVars=list("pctCovidICU"=c("label"="Covid", "color"="red"),
"pctICU"=c("label"="Total", "color"="black")
),
fullList=list("pctICU"=expression(icu_beds_occupied/icu_beds),
"pctCovidICU"=expression(adult_icu_covid/icu_beds)
),
plotTitle="Average % ICU Capacity Filled by Week",
plotSubtitle="August 2020 to December 2021",
plotScaleLabel="% ICU\nUsed",
returnData=TRUE
)
## # A tibble: 3,774 x 13
## state collection_week total_beds adult_beds adult_beds_occu~ adult_beds_covid
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 AK 2020-07-31 1670. 356. 516. 32.1
## 2 AK 2020-08-07 1667. 356 534. 38
## 3 AK 2020-08-14 1659 356. 526. 26.3
## 4 AK 2020-08-21 1662. 476. 496. 24.9
## 5 AK 2020-08-28 1651. 475. 496. 20.6
## 6 AK 2020-09-04 1608 506 510. 20
## 7 AK 2020-09-11 1631. 506 550. 25.6
## 8 AK 2020-09-18 1589. 506 378. 32.7
## 9 AK 2020-09-25 1502 506 661. 29.8
## 10 AK 2020-10-02 1527. 937 651. 28.5
## # ... with 3,764 more rows, and 7 more variables: inpatient_beds <dbl>,
## # icu_beds <dbl>, icu_beds_occupied <dbl>, adult_icu_covid <dbl>, n <int>,
## # pctICU <dbl>, pctCovidICU <dbl># Adult beds summary
createGeoMap(stateHosp_20220109,
yVars=list("pctCovidAdult"=c("label"="Covid", "color"="red"),
"pctAdult"=c("label"="Total", "color"="black")
),
fullList=list("pctAdult"=expression(adult_beds_occupied/adult_beds),
"pctCovidAdult"=expression(adult_beds_covid/adult_beds)
),
plotTitle="Average % Adult Beds Capacity Filled by Week",
plotSubtitle="August 2020 to December 2021",
plotScaleLabel="% Adult\nBeds\nUsed",
returnData=TRUE
)
## # A tibble: 3,774 x 13
## state collection_week total_beds adult_beds adult_beds_occu~ adult_beds_covid
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 AK 2020-07-31 1670. 356. 516. 32.1
## 2 AK 2020-08-07 1667. 356 534. 38
## 3 AK 2020-08-14 1659 356. 526. 26.3
## 4 AK 2020-08-21 1662. 476. 496. 24.9
## 5 AK 2020-08-28 1651. 475. 496. 20.6
## 6 AK 2020-09-04 1608 506 510. 20
## 7 AK 2020-09-11 1631. 506 550. 25.6
## 8 AK 2020-09-18 1589. 506 378. 32.7
## 9 AK 2020-09-25 1502 506 661. 29.8
## 10 AK 2020-10-02 1527. 937 651. 28.5
## # ... with 3,764 more rows, and 7 more variables: inpatient_beds <dbl>,
## # icu_beds <dbl>, icu_beds_occupied <dbl>, adult_icu_covid <dbl>, n <int>,
## # pctAdult <dbl>, pctCovidAdult <dbl>